ecological model essay examples

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Article Contents

The conceptual model, the role of quantitative models in ecological research, when should a quantitative model be developed, building quantitative ecological models, nuts and bolts of assembling a quantitative ecological model, deterministic or stochastic, a way forward, acknowledgments, references cited, common pitfalls and potential solutions, decisions about model implementations.

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An Introduction to the Practice of Ecological Modeling

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Leland J. Jackson, Anett S. Trebitz, Kathryn L. Cottingham, An Introduction to the Practice of Ecological Modeling, BioScience , Volume 50, Issue 8, August 2000, Pages 694–706, https://doi.org/10.1641/0006-3568(2000)050[0694:AITTPO]2.0.CO;2

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Modeling has become an important tool in the study of ecological systems, as a scan of the table of contents of any major ecological journal makes abundantly clear. A number of books have recently been published that provide excellent advice on model construction, building, and use (e.g., Gotelli 1995 , Gurney and Nisbet 1998 , Roughgarden 1998 ) and add to the classic literature on modeling ecological systems and their dynamics (e.g., Maynard Smith 1974 , Nisbet and Gurney 1982 ). Unfortunately, however, littleany—of this growing literature on ecological modeling addresses the motivation to model and the initial stages of the modeling process, information that beginning students would find useful.

Fast computers and graphical software packages have removed much of the drudgery of creating models with a programming language and opened new avenues of model construction, use, and even misuse. There are many reasons why a student might want to consider modeling as a component of his or her education. Models provide an opportunity to explore ideas regarding ecological systems that it may not be possible to field-test for logistical, political, or financial reasons. Often, learning occurs from apparently strange results and unexpected surprises. The process of formulating an ecological model is extremely helpful for organizing one's thinking, bringing hidden assumptions to light, and identifying data needs. More and more, students want to “do something” with modeling but are not sure how to get started.

The goals of this article are to outline issues concerning the value of ecological models and some possible motivations for modeling, and to provide an entry point to the established modeling literature so that those who are beginning to think about using models in their research can integrate modeling usefully. We therefore envision the typical reader to be an advanced undergraduate, a beginning graduate student, or a new modeler. We first consider some of the values of models and the motivation for modeling. We then discuss the steps involved in developing a model from an initial idea to something that is implemented on a computer, outlining some of the decisions that must be made along the way. Many excellent texts and journal articles deal with the technical details of models and model construction; we do not attempt to replace this literature, but rather try to make the reader aware of the issues that must be considered and point to some of the sources we have found particularly useful.

We begin with the assumption that the reader has decided that he or she would like to “do something” with modeling as part of his or her research (Figure 1) . It is important to recognize the difference between models and the modeling process. A model is a representation of a particular thing, idea, or condition. Models can be as simple as a verbal statement about a subject or two boxes connected by an arrow to represent some relationship. Alternatively, models can be extremely complex and detailed, such as a mathematical description of the pathways of nitrogen transformations within ecosystems. The modeling process is the series of steps taken to convert an idea first into a conceptual model and then into a quantitative model. Because part of what ecologists do is revise hypotheses and collect new data, the model and the view of nature that it represents often undergo many changes from the initial conception to what is deemed the final product.

The discussion that follows is organized to consider issues in a sequence similar to what a new modeler would encounter. Because individuals' backgrounds differ, the sequence is not fixed. We map one possible route through the sorts of decisions that will most likely need to be considered; this course is derived from our individual experiences plus the collective knowledge of our reviewers. We begin with conceptual models because many people, even self-labeled nonmodelers, formulate conceptual models.

The development of a conceptual model can be an integral part of designing and carrying out any research project. Conceptual models are generally written as diagrams with boxes and arrows, thereby providing a compact, visual statement of a research problem that helps determine the questions to ask and the part of the system to study. The boxes represent state variables , which describe the state or condition of the ecosystem components. The arrows illustrate relationships among state variables, such as the movement of materials and energy (called flows ) or ecological interactions (e.g., competition). Shoemaker (1977) provides an excellent discussion about how to develop conceptual models.

The development of a conceptual model is an iterative process. The skeleton of a conceptual model begins to take shape when a general research question is formulated. For example, suppose the goal of a research project is to determine the relationship between different strategies for stocking exotic salmon in the Great Lakes and the concentrations of potentially toxic contaminants in the salmon and their alewife prey. The initial conceptual model might consist of two linked boxes labeled “alewife” and “chinook salmon,” with an additional arrow labeled “stocking” pointing to the salmon's box (Figure 2a) . We have chosen to place two-way arrows between the boxes to reflect the flow of biomass and contaminants from alewife to salmon and the effect of salmon on the alewife; an alternative model might have used only one arrow, since the flow of material between boxes is the result of predation by salmon on alewife. Details would then be added to the conceptual model based on the answers to questions such as, Are there other important species besides alewife and chinook salmon? What mechanistic processes should be included? What environmental factors influence each species? What currency should be used to describe compartment interactions (e.g., elements, biomass, individuals, energy)?

After making refinements driven by such questions, the conceptual model might have alewife, chinook salmon, rainbow smelt, and lake trout (Figure 2b) , although the research interest might still be with the original two species. The next round of refinements to the conceptual model might be based on available data or consultation with ecologists who have studied the interactions of the four species shown in Figure 2b . For example, if contaminant concentrations are a function of prey body size, and if predators seek certain size classes of prey, then size structure might be added to the model to more accurately reflect these ecological features and to better simulate contaminant intake by predators (Figure 2c) . Depending on the nature of the research question, the addition of size structure might be made for just the alewife and chinook salmon. This simple example assumes that there are changes only in the state variables, but there could also be changes in the relationships among the state variables.

In general, a parsimonious approach is best for creating an appropriate conceptual model. The model should strike a balance between incorporating enough detail to capture the necessary ecological structure and processes and being simple enough to be useful in generating hypotheses and organizing one's thoughts. Creating a good conceptual model forces an ecologist to formulate hypotheses, determine what data are available and what data are needed, and assess the degree of understanding about key components of the system. Because outside viewpoints and questions often force clarification of biases and assumptions, discussing the evolving conceptual model with colleagues can be helpful. Group construction of a conceptual model can also be a useful consensus-building tool in collaborative research ( Walters 1986 , Carpenter 1992 ). Conceptual models should therefore be included in dissertation and grant proposals, especially in the early stages of project development. Revisions of the initial conceptual model then become focal points for discussion in subsequent meetings of the dissertation committee or research planning group.

A quantitative model is a set of mathematical expressions for which coefficients and data have been attached to the boxes and arrows of conceptual models; with those coefficients and data in place, predictions can be made for the value of state variables under particular circumstances. Ecologists use quantitative models for various purposes, including explaining existing data, formulating predictions, and guiding research. Simple quantitative models can be solved with pencil and paper (see mathematical ecology textbooks such as Pielou 1977 , Hallam and Levin 1986 , and Edelstein-Keshet 1988 ), but most ecological models are now implemented on a computer.

Quantitative ecological models can guide research in a number of ways. Constructing a quantitative model and running simulations may help in the design of experiments ( Carpenter 1989 , Hilborn and Mangel 1997 ), for example, to evaluate experimental power for different hypothesized effect sizes. Sensitivity analysis of a quantitative model can reveal which processes and coefficients have the most influence on observed results and therefore suggest how to prioritize sampling efforts. Quantitative models can even be used to generate “surrogate” data on which to test potential environmental indicators or evaluate potential sampling schemes. Most important, quantitative models translate ecological hypotheses into predictions that can be evaluated in light of existing or new data.

Ecologists often use quantitative models to formulate predictions about the systems they study. Some predictive models are empirical, meaning that they represent relationships determined strictly by data. Because empirical models are not based on a knowledge of underlying mechanisms, they are most useful within the bounds of the data with which they are developed ( Weiner 1995 ). A well-known empirical model from aquatic ecology predicts the level of summer chlorophyll from spring total phosphorus ( Dillon and Rigler 1974 ). Other predictive models are more mechanistic, based on hypotheses about the particular ecological processes that cause an observed pattern. The incorporation of key ecological features, such as size-selective predation and increasing contaminant concentrations with increasing prey body size (to use an example similar to that in Figure 2 ), leads to the prediction of a tradeoff between decreasing concentrations of PCBs in salmon and the probability of survival of salmon prey (Figure 3; Jackson 1997 ). In the absence of these mechanistic ecological details, lower contaminant concentrations are predicted in predators ( Jackson 1996a , 1996b ).

Predictive models can become quite complex, especially when their forecasts are used as the basis for resource management and policy decisions. Examples include global climate models, fisheries management models for setting catch and harvest quotas, watershed management models for nutrient control strategies, and risk assessment models for environmental engineering. Often, these complex predictive models are used to generate predictions for scenarios for which actual tests are difficult or impossible to run for ecological, social, or economic reasons.

Like a conceptual model, a quantitative model is rarely an end in itself. Often learning results from considering a changing suite of several quantitative models, or several formulations of processes within a particular model ( Pascual et al. 1997 ). The assessment of different models and processes allows an evaluation of the assumptions specific to those formulations and processes. In this context, it is useful to remember that models are only tools and not reality, and there is no “correct” model.

Models should follow from specific research questions rather than questions following from models. Thus, the decision to build a quantitative model from a conceptual model should occur only after a clear, focused research question has been distilled from initial ideas. A full-scale quantitative model should be created only when each of the following questions can be answered with a yes:

Will a quantitative model add to the scientific content of the study?

Is there sufficient motivation to devote the necessary time to develop a quantitative model?

Will the investment in modeling enhance the quality of knowledge produced?

There are clear advantages to the incorporation of quantitative modeling in a research program. We have already touched on some of these benefits, such as formulating predictions and identifying data needs or knowledge gaps. Models are also useful for organizing one's thinking about a problem. Once a conceptual model is converted to a quantitative model and used, new questions may arise as a result of interesting and unexpected results. However, the time it takes to build a useful quantitative model should not be underestimated. Model building becomes easier with practice, but modelers should expect to spend several weeks or months constructing, parameterizing, testing, and running a modestly complex model. (The time spent depends to some degree on the software used, which is discussed more below.)

Once an ecologist has decided to build a quantitative model, how should he or she choose the type of model to build? Some general classes of models used in ecology include energy and mass balance models (e.g., Hewett 1989 ), population genetics models (e.g., Roughgarden 1979 ), optimization and game theory models (e.g., Mangel and Clark 1988 ), individual-based population models (e.g., DeAngelis and Gross 1992 ), size- or age-structured population models (e.g., Caswell 1989 ), community and ecosystem models (e.g., Scavia and Robertson 1980 ), and landscape models (e.g., Baker 1989 ). Because the degree of detail varies widely within these broad categorizations (Table 1) , we recommend reading papers that discuss the merits of various modeling approaches (e.g., Levins 1966 , DeAngelis and Waterhouse 1987 , DeAngelis 1988 ). An overview of model types and formulations can also be obtained from a survey course in mathematical modeling, and we strongly recommend taking such a course as soon as the idea to “do something” with models arises. The specific types of models being considered may suggest further course work. For example, differential equations are used in many models, matrix algebra underlies size- and age-structured models, and geographical information systems (GIS) are needed to work with many spatial and metapopulation models.

The choice of model type and detail will depend on the system studied, the questions asked, and the data available. Quantitative models can quickly become complex and clear problem definition is essential to keeping the model focused. A good conceptual model is invaluable for deciding what ecological detail to include and what to ignore. For example, suppose an ecologist is studying two forest stands: One stand is intact, whereas a presumedly important seed disperser has been removed from the other. Has the removal of the seed-dispersing animal caused any changes in the population of a particular tree species in the experimental stand? There are several ways in which quantitative modeling can be used to address this question. A simple age-structured model (e.g., Caswell 1989 ) of the tree population may be useful if the ecologist wants to look for changes in age structure. Alternatively, a spatially explicit model might be needed if the ecologist wants to explore differences in spatial pattern. If the ultimate goal is to test the findings from the quantitative model in the field, then the model that is developed will dictate the types of data that will need to be collected from the two forest stands.

Once the general type of quantitative model has been chosen, the ecologist must determine the appropriate level of abstraction for the model. Consulting papers on the value of simple ( Fagerström 1987 , Scheffer and Beets 1993 ) versus complex ( Logan 1994 ) models may help guide this decision. Good models never include all possible compartments and interactions ( Fagerström 1987 , Starfield 1997 ), and the complexity of a model depends very much on the purpose and question addressed by the model. There are tradeoffs between the generality of a model and its practical utility for a particular situation ( Levins 1966 ). A highly abstract model with few parameters may be best to test general ecological hypotheses. However, for specific questions, such as whether changes in fire frequency have affected the spatial pattern of a species, a detailed spatial model coupled to GIS data may be required. Thus, a model's structure should be consistent with both the question(s) asked and the measurements made ( Costanza and Sklar 1985 , Ludwig and Walters 1985 , DeAngelis et al. 1990 ). Data for many populations are collected by size or developmental stage or at fixed time intervals, leading naturally to models with size or stage structure and certain time steps (see the box on page 700 for more on time steps). With too little detail in the model, the mechanisms driving the response of interest may not be captured. On the other hand, too much detail makes a model difficult to parameterize (determine coefficients for equations) and to validate ( Beck 1983 , Ludwig and Walters 1985 , DeAngelis et al. 1990 ). An active area of research therefore considers how to reduce model complexity while retaining essential system behavior ( Rastetter et al. 1992 , Cale 1995 ).

Once the decision to build a quantitative model has been made, and issues of model complexity and structure have been dealt with, it is necessary to develop algebraic formulations (equations) for model processes, to establish means for solving them, and to choose parameters for each equation before implementing the model on a computer. Thinking about these issues in advance may save a modeler from having to go back and redevelop portions of the model.

The importance of keeping good notes

The litmus test for a model description is that a relatively experienced modeler must be able to reproduce the model and its output, just as experiments should be capable of being replicated. Therefore, it is important to document decisions about equation forms, parameter values, and computational details, as well as any sources of information used to make these decisions. Good notes taken during model building will save hours combing the literature to rediscover the source of assumptions or parameter values.

Choosing equations

One of the initial steps in converting a conceptual model to a quantitative model involves quantifying the arrows between the state variables. This process actually involves two steps: choosing appropriate equations and determining the parameters for those equations. Equations represent mathematically the interactions among or transfers of energy or materials between state variables in a model. For example, equations 1 , 2 , and 3 represented different (hypothesized) ways to describe the process of predator consumption. Parameters are constants in the equations that make the algebraic expressions correspond to actual data.

Equations appropriate to a particular situation may be available in the literature. Certain constructs (e.g., feeding relationships, energetic equations) are common to many ecological models, although they may need to be reparameterized for different systems. Many relationships can be found in modeling textbooks, including Models in Ecology ( Maynard Smith 1974 ), Ecological Implications of Body Size ( Peters 1983 ), Handbook of Ecological Parameters and Ecotoxicology ( Jorgensen et al. 1991 ), Dynamics of Nutrient Cycling and Food Webs ( DeAngelis 1992 ), A Primer of Ecology ( Gotelli 1995 ), and Primer of Ecological Theory ( Roughgarden 1998 ). First principles (i.e., physical laws) can also provide useful relationships. Mathematically important differences among alternative formulations may or may not be important for a particular situation. If the particular form of an equation is of concern, the effects of each formulation on model results can be explored as part of the modeling exercise.

Computational issues associated with equations

Difference equations are simply solved by recursion; that is, later predictions depend on earlier predictions. Differential equations describe continuous processes, but must nevertheless be solved in discrete time steps on a computer. The two principal methods used to solve differential equations are the Euler and the Runge-Kutta methods. The Euler method steps through the differential equation as if it were a difference equation by using information at the beginning of each time interval to calculate values at the next time interval. The Euler method can be unstable when the interval between solutions (the step size) is small, because rapid accumulation of errors prevents convergence on the real solution. The Euler method may also be unstable at large step sizes because small changes in rates and local maxima and minima in the solution may be missed, which can be particularly problematic if the differential equations are nonlinear ( Press et al. 1992 ). Runge-Kutta algorithms also start with the information at the beginning of a time interval but then sample the solution at several points between the beginning and end of the interval. For most differential equation models, the Runge-Kutta is more accurate than the Euler method, and fourth-order Runge-Kutta is particularly recommended ( Press et al. 1992 ). Graphical and algebraic explanations of the Euler and Runge-Kutta algorithms appear in Press et al. (1992 ) and in textbooks on numeric methods in computing (e.g., Atkinson 1989 ). Variable step-size methods can be used to find the optimum balance between accuracy and computational speed by using small step sizes when variables are rapidly changing and long step sizes when variables are changing slowly.

A deterministic model has no random components; for the same initial conditions and time period projected, it always gives the same result. In contrast, a stochastic model incorporates at least one random factor, and thus the results are different every time the model is run. One type of stochastic model assumes that the values of some or all parameters vary through time or across individuals and are therefore described by probability distributions. Each time the model is run, the parameter values are drawn from their specified probability distributions. Other stochastic models add random errors following each calculation to simulate the effects of environmental variability. One reason to add stochasticity is to produce realistic variability in the trajectories of the state variables through time, either because the variance as well as the average value is of interest or because the effect of variability in one state variable on another state variable is of interest. Model results might be cast in terms of probabilities—for example, as the percentage of simulations in which a certain outcome (such as a catastrophic population crash) was attained. A stochastic model is not necessarily more “correct” than a deterministic model, and it is more work to create. It does provide additional information, but whether this information is of value depends on the purpose of the model. We recommend Nisbet and Gurney (1982) as the starting point for an introduction to deterministic and stochastic models.

Selecting modeling software

Implementation of a quantitative model on a computer requires the modeler (or the computer program) to keep track of many details. Some of these details, while necessary for the model to run, are irrelevant to the model predictions (e.g., allocating computer memory for arrays and matrices, creating a user interface, and writing output). Other details, such as how variables are initialized, how random numbers are generated, the order in which equations are solved, and the algorithm (computer instructions) used for solving them, do affect the predictions. We discuss some of these details further in the boxes on FPAGE 697 and 699.

The computer software selected should be determined by the degree to which the modeler wishes to control these details. At one extreme are general programming languages (e.g., C, Basic, Fortran, Pascal) that allow the modeler complete control over the model construction but also require the modeler to handle all of the sometimes tedious details. Model building gets easier with practice and by reusing bits of previously generated code, but it can still be quite time-consuming even for relatively experienced programmers. Prewritten routines for random numbers, matrix algebra, and other algorithms are available for most programming languages, reducing the need to reinvent some wheels (e.g., Numerical Recipes; Press et al. 1992 ). If this option is chosen, coursework in at least one programming language might be helpful; general programming concepts and skills translate across languages.

At the other extreme are graphical programs (e.g., STELLA, SimuLink, ModelMaker) that allow the user to create the computer program (the model) by choosing icons from a menu while the software handles the details. Models can be constructed quickly, but there are limits on what can be built and the implementation details are often hidden from the user. This final point is a significant weakness of graphical modeling packages, and we therefore tend to create our own models using programming languages. However, intelligent use of modeling packages can permit incorporation of modeling into a study with far less effort than building a model from scratch.

Between these two extremes are programming packages that include functions to handle many of the details but still leave some control to the modeler (e.g., Matlab; see Roughgarden 1998 ) and spreadsheets (e.g., Excel; see Weldon 1999 ). This intermediate approach may appeal to those who want to know how equations are being solved without becoming mired in the syntax of a programming language.

Parameter estimation and model calibration

Parameter estimation is the process of finding parameter values for each equation in the quantitative model. The source of parameter values depends on how the model is going to be used. If the model is being developed to explore the consequences of different parameter values, then the model will be run for a wide range of different parameters without reference to particular ecological systems. However, if a model is being developed to predict behavior in a particular system, then usually a single (mean) value will be chosen for each parameter. In this case, parameter values are estimated by fitting equations to the data from the system, or perhaps from data available in the literature. Sometimes data are not available, in which case a modeler might estimate parameters by an iterative process of matching model output to observed system behavior. This latter practice is referred to as tuning (calibration) by direct search, and the parameters are altered until the model produces a reasonable fit with observations of the state variables. Tuning can be done systematically or by trial and error. Either way, keeping good notes is essential. Parameters determined by direct search are best viewed as hypotheses to be tested as data become available.

When parameters are estimated from observed data, the modeler seeks the parameters that lead to the best fit between an equation and the observed data (e.g., Hilborn and Mangel 1997 ). The least-squares criterion and maximum likelihood estimation are the two most commonly employed methods for this kind of parameter estimation. Least-squares estimates of parameters minimize the value of the squared deviations between the simulated and observed data; these estimates can be used for just about any deterministic component of a model for which distributions are near normal and variance is constant throughout the range of an independent variable ( Brown and Rothery 1993 ). However, for models that are nonlinear in the parameters, least squares may produce biased parameter estimates; for these models, maximum likelihood may yield better parameter estimates. Maximum likelihood algorithms determine the parameter values that maximize the probability that the observations would have occurred if the parameters were correct ( Hilborn and Walters 1992 ). Unlike least squares, maximum likelihood does not require that error terms be normally distributed ( Hilborn and Mangel 1997 ). It is beyond the scope of this article to review parameter estimation techniques, but useful information on that subject can be found in Draper and Smith (1981) , Hilborn and Walters (1992) , and Hilborn and Mangel (1997) .

Debugging, sensitivity analysis, and validation

Once a quantitative model is assembled, it must be tested to ensure that it is functioning properly; that process is called “debugging.” We recommend that the equations be calculated by hand to ensure that the code is performing as it should—that is, arrays and matrices are properly indexed, equations are properly calculated, and so forth. Each module or subroutine of a model developed with a programming language should be tested separately before the completed model is run. Output should be tabulated, state variables graphed, and intermediate parameter and rate values monitored to ensure that they are realistic during simulations. One also should check that the model behaves as expected in situations for which the analytical solution is known.

Sensitivity analysis explores whether the conclusions would change if the parameters, initial values, or equations were different. Consequently, sensitivity analyses can be used to guide further research (for example, to identify those parameters that would be worth the investment of additional field measurements or experiments), to corroborate the model, and to improve parameter estimates. There are three basic approaches to sensitivity analysis: varying parameter values one at a time, systematic sampling, and random sampling ( Hamby 1994 ). Swartzman and Kaluzny (1987) provide an excellent discussion of the advantages and disadvantages of each of these approaches. The simplest sensitivity analysis examines the effect of each parameter on model dynamics individually ( Bartell et al. 1986 ). The model is typically deemed sensitive to a particular parameter if changing that parameter's value by 10% leads to more than a 10% change in the output from the baseline scenario. Because analysis of one parameter at a time will not identify sensitive interactions among parameters, it may also be worthwhile to explore the effects of variation in two or more parameters at the same time using either systematic or random sampling ( Swartzman and Kaluzny 1987 ). When many parameters may interact, random sampling may be the best approach. Random sampling is most often done with Monte Carlo techniques (e.g., Swartzman and Kaluzny 1987 , Bartell et al. 1988 ), whereby, during each of perhaps 1000 model runs, a value for each parameter is “sampled” from a range or probability distribution. Model runs then undergo partial correlation analyses, which yield estimates of the contribution of each parameter to the overall variance in the output. Parameters with high partial correlations have the most influence on results.

In addition to doing a sensitivity analysis on parameter values, the model should be checked for sensitivity to initial conditions and equations. For example, the model can be initialized with different species ratios or size structures to find out whether output is driven by these choices. Model sensitivity to alternative equations for relationships among state variables can also be checked by rerunning the model with different equations and seeing whether the conclusions change.

Once a model works, the modeler may need to ask whether it sufficiently resembles reality, but whether that question can be answered at all is a matter of considerable philosophical debate ( Mankin et al. 1975 , Oreskes et al. 1994 , Rastetter 1996 , Rykiel 1996 ). Nevertheless, at some point the researcher must decide that the model is good enough and no more tinkering is necessary. For many system-specific ecological models, this decision is made based on comparisons of simulated data with field or experimental data. If the simulated data are sufficiently similar to the observed data, then the model is judged to be validated or corroborated, and simulations with the model proceed. If the simulated data do not match the observed data, then further work is necessary. Objective criteria for model validation include the standard error of model predictions and the proportion of variance explained by the model ( Caswell 1976 , Power 1993 ). It is preferable to have independent data for model corroboration and calibration, although in practice independent data are often hard to find, particularly for whole ecosystems.

Modeling offers exciting possibilities for the exploration of ideas that are not easily pursued through field experimentation or laboratory studies. Ecologists, for example, use models to simulate the systems they study and to investigate general theories of the way those systems operate. Moreover, simulation of systems with models helps identify data needs and knowledge gaps.

Many research programs can benefit from the integration and development of conceptual and quantitative models. The process of creating a conceptual model begins with a question; from there, the researcher formulates hypotheses, evaluates available and needed data, and assesses the degree of understanding of the system under consideration. Then the conceptual model is converted to a quantitative model; that process is iterative, evolving as new data and ideas are discovered.

We cannot possibly cover every aspect of ecological modeling—which is both a skill and a process—in one short article. We do hope, however, that we have successfully raised the issues that a beginning modeler must consider, provided an entry point to the modeling literature, and discussed the role of modeling in ecological research.

We thank Steve Carpenter for numerous suggestions during the writing of the manuscript. We are grateful to many people at the Center for Limnology, University of Wisconsin–Madison, for support during our model building years there (especially David Christensen, Xi He, Daniel Schindler, Craig Stow, and Rusty Wright). We thank Steve Carpenter, George Gertner, Lloyd Goldwasser, Bruce Kendall, Russell Kreis, Bill Nelson, John Nichols, Daniel Schindler, and, in particular, Rebecca Chasan, Wayne Getz, and an anonymous reviewer for their thoughtful reviews of the manuscript. L. J. J.'s research with simulation models was funded by the Natural Sciences and Engineering Research Council of Canada and by the Wisconsin Sea Grant Institute under grants from the National Sea Grant College Program, National Oceanic and Atmospheric Administration, US Department of Commerce, and from the State of Wisconsin (Federal grant NA90AA-D-SG469, project R/MW-41). K. L. C.'s initial research with simulation models was funded by a predoctoral fellowship from the National Science Foundation. K. L. C. also thanks the National Center for Ecological Analysis and Synthesis, which is funded by NSF (DEB94-21535); the University of California at Santa Barbara; and the State of California for financial and logistical support while preparing this paper for publication.

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This troubleshooting box outlines some common mistakes made during model construction. It is not an exhaustive list. We hope that the novice modeler will profit from our experience in solving these problems, which arise largely from writing one's own code in a programming language.

Pay careful attention to units, scaling, and conversions. For example, translating prey eaten by one trophic level (units of mass) to a mortality rate for another (numbers) requires a conversion and change of units. We go through our equations and write the dimensions and units to ensure that we are making appropriate conversions. Units and dimensions for empirically derived relationships tend to be built into regression parameters (e.g., ungulate biomass [kg] derived from grass productivity [g · m −2 · d −1 of carbon]). Problems often arise when different state variables operate on different spatial scales, which is sometimes less obvious than when the variabes operate on different time scales. Fish, for example, occupy a volume (g · m −3 ) but may eat benthic invertebrates that occupy a surface (g · m −2 ), requiring rescaling when computing trophic transfers. Apparent conversion problems can also be caused by failure to properly share variables among subroutines.

Be careful with time steps and model stability, especially for models with differential equations. The modeler typically must choose a single step size (e.g., hourly, daily, monthly, yearly) over which to have the algorithm solve the equations, even though the time step appropriate for evaluating one process (e.g., hourly nutrient uptake by phytoplankton) may not be appropriate for evaluating another (e.g., annual growth of fishes). Equations whose dynamics suffer when independent variables change on widely disparate time scales are known as “stiff” equations. Problems often occur because small roundoff or truncation errors in one variable lead to enormously inflated errors in another; such problems can be diagnosed by evaluating output variables at a variety of step sizes. An alternative approach to manually manipulating step size is to use an algorithm with an adaptive step size ( Press et al. 1992 ), which gives smoother dynamics but takes more work to program. One can also explicitly divide the model into “fast” and “slow” components and then update the fast components much more frequently than the slow components.

Pay attention to setting and resetting values. Arrays and matrices are a common source of computer bugs, thus warranting extra attention to their dimensioning, initializing, and indexing. We assign values to parameters before they are used rather than relying on the software to initialize them. We also check that parameters and initial conditions obtained from an input file are properly read and assigned. After the lapse of important time periods, we check that variables have been zeroed or renewed as appropriate. For example, in a model in which seed germination for a plant proceeds only when certain environmental conditions are met, the value for seedlings should be set to zero each time germination fails rather than (unintentionally) taking the value from the previous year. Similarly, when all individuals in a particular size or age class die or are eaten, the variables tracking their characteristics must be properly reset to prevent carryover effects when a new cohort arrives. Populations modeled with real numbers will approach but not equal zero when subjected to a constant mortality rate, and should be set to zero after some minimum population size is attained. Inspecting graphs of state variables will elucidate what is happening.

Test random number generators before using them. Random number generators vary in quality and should be tested before use. A statistics package can be used to analyze the results of 10,000 or so sequential random numbers to ensure that the mean, standard deviation, and distribution are as specified and the shape is as expected. If qualitatively different results occur when initializing the random number generator at the beginning of the program versus the beginning of each replicate, we look for another random number generator. We recommend reading Press et al.'s (1992 ) discussion of random-number generating algorithms. One way to keep random numbers the same from run to run, which is useful when developing or debugging a model, is to start each simulation with the same “seed” (the initial number from which the random numbers are generated). When the time comes to use different seeds, the computer's clock can be used for the seed value.

Issues concerning how numbers are stored and updated, how calculations are sequenced, and how inputs and outputs are made may seem unimportant to the novice modeler, but our experience is that computational details merit attention early in the modeling process because they can have substantial implications for model use and behavior.

The nature of inputs and outputs determines how easily a model is used and analyzed. If inputs are part of the model code, the model must be recompiled (translated from text into instructions the computer executes) each time the inputs are changed. If inputs are read in as a separate file (which takes more work to program), the model can be run many times with different inputs without recompiling. It is worth formatting output with the planned analysis in mind—select formats amenable to processing with statistical or graphics software. Excessive output slows the simulation time, but representative subsets of intermediate calculations should be inspected to ensure that everything is reasonable.

The sequence in which events proceed can affect results. Events that happen simultaneously in nature must occur in sequence in computer models. For example, if the organism or size class that is first in numerical order in a vector of state variables is always the first for which foraging is evaluated, it may unintentionally be the one that gets the most food!

Separating old from new values allows sequential calculations of simultaneous events to proceed correctly. Newly calculated values should be assigned to temporary variables so that subsequent calculations are not based on a mixture of old and new state variables. The value of the state variables should be updated with the values in the temporary variables only after all calculations have been completed for that time step.

Decide whether to model populations as whole or real numbers. Neither choice is perfect. Using real numbers gives fractions of individuals, whereas using integers presents stochasticity and rounding problems. For example, if the number of survivors is calculated by multiplying the survival rate by the number of starting individuals and then rounding to the nearest integer, then a single individual with a survival rate of 0.8 will live forever! It would be better to use 0.8 as a probability and then do the equivalent of flipping a coin—that is, draw a random number.

Decide how many stability checks and assurances to build into a model. The inherent mathematical and architectural constraints of computers can lead to unexpected model behavior ( Acton 1996 ). It is important to anticipate both mathematically illegal operations (e.g., division by zero) that would cause the simulation to crash and circumstances that would cause the simulation to become invalid. For example, it might be appropriate to stop the simulation if one species in a multispecies model goes extinct, to build in a means for its reestablishment if it goes extinct, or to build in a refuge or alternate food supply so that extinction is prevented. These types of stability guarantees should be used prudently. Excessive stabilizing components can hide programming errors or even dominate model dynamics; on the other hand, if used sparingly, they can prevent the frustration of having a long simulation rendered useless by a circumstance for which a stability check could easily have been programmed.

Table 1. Ecological models for representing populations

Figure 1. Flow chart summarizing the process of creating an ecological simulation model. The model building process distills current knowledge into a conceptual framework, which forms the scaffolding for the model's construction. A number of steps involve iterations or refinements that follow from consulting data, experienced modelers, or other ecologists. Once there is output from the model, the original idea or state of knowledge may be modified and additional model refinements, data collection or experiments might be planned. Benefits of the modeling process include eliminating alternatives, identifying gaps in knowledge, identifying testable hypotheses, and indicating avenues for additional experimentation and data collection

Figure 2. Example of the iterative nature of building a conceptual model from an initial idea. The first iteration (a) describes a simple relationship between one predator and prey. One arrow identifies biomass and contaminants as the material flowing from alewife to chinook salmon, and the other arrow identifies predation as an important ecological process structuring the alewife population. In this example, interest is in how the rate at which salmon are stocked affects the relationship between salmon and alewife. Additional information at the second iteration might indicate that the dynamics of the salmon and alewife (a) are also affected by rainbow smelt and lake trout, which are subsequently incorporated into the conceptual model (b). Finally, information on contaminant concentrations as a function of body size and more detail on predator preference of prey might indicate that age or size structure should be included (c). Depending on the goal of the modeling exercise, detailed age structure might be examined for the original two species of interest. In b and c, the double-headed arrows indicate state variables that directly interact. In c, the wide gray arrows represent the movement of fish to older age classes. Box labels represent the age of fish; YOY are young-of-year. Two quantitative models might be constructed: one for conceptual model b and one for conceptual model c

Figure 3. PCB concentrations (solid line) of age class 4+ chinook salmon and the probability of an alewife population crash (dashed line) for chinook salmon stocking rates and a Shepherd stock-recruitment relationship. PCB concentrations are the result of 200 model runs to year 2015, at each stocking rate, based on bootstrapped estimates of the Shepherd stock-recruitment relationship from 14 years of data for Lake Ontario. The arrow indicates 1994 stocking rates. The dotted line around the chinook salmon PCB concentrations represents +/− 2 SE

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Ecological Theory: Bronfenbrenner’s Five Systems

Categories Development , Theories

Ecological theory suggests that human development is influenced by several interrelated environmental systems. Introduced by psychologist Urie Bronfenbrenner, ecological theory emphasizes the importance of understanding how various systems and environments interact with and influence people throughout life. 

Key Takeaways

• Ecological theory examines how individuals are shaped by their interactions with various environments.
• Bronfenbrenner’s model categorizes these environments into microsystems, mesosystems, exosystems, macrosystems, and chronosystems.
• The theory highlights the importance of considering environmental context in understanding human development.
• While offering valuable insights, ecological theory also poses challenges, such as complexity and limitations in generalization.

Table of Contents

What Is Ecological Theory?

Bronfenfrenner’s ecological theory suggests that the interaction between and individual and their environment influences the developmental process. Bronfenbrenner organized these environmental factors into different systems or layers–each one interacting with each other as well as the individual.

In order to understand how humans develop throughout life, it is important to examine the multiple connections and influences of such systems. These influences include the immediate environment, including family and peers, as well as the much broader society and culture in which the individual and these other systems exist.

The Five Systems in Ecological Theory

Ecological theory describes five layered systems or levels that influence human behavior and development. These levels are often portrayed as a series of concentric circles. At the center of the system is the individual. The first layer is the one that they have the most immediate contact with, with each circle expanding outward and encompassing all of the inner layers.

The five levels of ecological theory are the microsystem, mesosystem, exosystem, macrosystem, and chronosystem.

1. Microsystem

The microsystem refers to the immediate environments where individuals directly interact, such as family, school, peer groups, and religious institutions. These settings have a profound impact on a person’s development, as they provide the most immediate and intimate social experiences. 

For example, within the family microsystem, children learn essential skills, values, and behaviors through interactions with parents, siblings, and caregivers. Similarly, the school microsystem shapes cognitive development, social skills, and peer relationships. 

These microsystemic interactions are crucial as they lay the foundation for future relationships and societal engagement.

2. Mesosystem

The mesosystem encompasses the interconnections between various microsystems in an individual’s life. It focuses on how different settings interact and influence each other, ultimately impacting the individual’s development. 

For instance, the relationship between family and school is a significant aspect of the mesosystem. A child’s experiences at home can affect their performance and behavior at school, and conversely, school experiences can influence family dynamics. 

Understanding these interactions is essential for comprehending the holistic nature of human development and the interconnectedness of different environments.

3. Exosystem

The exosystem comprises external settings that indirectly impact an individual’s development, even though they do not directly participate in those settings. Examples include the parents’ workplace, community services, and mass media. 

These environments may influence the individual through the experiences of people close to them or through policies and societal norms. 

For instance, a parent’s job stability or workplace stress can affect family dynamics and, subsequently, a child’s well-being. Similarly, community resources and media portrayals can influence individuals indirectly and influence societal perceptions and values.

4. Macrosystem

The macrosystem encompasses the broader cultural, societal, and political contexts that influence development. It includes cultural norms, economic systems, ideologies, and government policies. These elements shape the values, beliefs, and opportunities available to individuals within a society. 

For example, cultural attitudes toward education, gender roles, and socioeconomic inequality significantly impact individuals’ life paths and opportunities. Understanding the macrosystem is crucial for recognizing the broader structural forces that shape human development and behavior.

5. Chronosystem

The chronosystem incorporates the dimension of time into Bronfenbrenner’s ecological theory, emphasizing how individual and environmental factors change over time and influence development. This system recognizes the importance of historical events, life transitions, and personal experiences at different developmental stages. 

For example, changes in family structure, societal norms, and technological advancements can profoundly affect individuals’ development across the lifespan. By considering these temporal factors, ecological theory provides a dynamic framework for understanding human development throughout the entire lifespan.

History of Ecological Systems Theory

Urie Bronfenbrenner was a renowned developmental psychologist. He introduced the ecological systems theory to provide a comprehensive framework for understanding human development. 

Born in 1917 in Russia, Bronfenbrenner immigrated to the United States with his family during the Russian Revolution. His early experiences as an immigrant deeply influenced his perspective on human development, leading him to explore the complex interactions between individuals and their environments.

Bronfenbrenner’s interest in understanding how various environmental factors shape development stemmed from his observations as a psychologist working with children and families. He sought to move beyond traditional theories that focused solely on individual traits or familial influences and instead emphasized the importance of considering the broader ecological contexts in which individuals live.

Bronfenbrenner developed his ecological systems theory throughout the latter half of the 20th century, drawing from interdisciplinary research in psychology, sociology, anthropology, and biology. He published his seminal work, “The Ecology of Human Development: Experiments by Nature and Design,” in 1979, where he presented his theory in detail.

Central to Bronfenbrenner’s theory is the notion that human development occurs within a series of nested environmental systems, each exerting varying degrees of influence on the individual.

Bronfenbrenner’s ecological systems theory has had a profound impact on the field of developmental psychology . It emphasizes the importance of considering the dynamic interplay between individuals and their environments. 

His work has influenced research, policy-making, and intervention strategies aimed at promoting healthy development across the lifespan. Urie Bronfenbrenner’s legacy continues to shape our understanding of human development and the complex ecological contexts in which it occurs.

Examples of Environmental Influences in Ecological Theory

To understand ecological theory, it can be helpful to take a closer look at some of the influences that people experience at each level:

Microsystem

• Family : Parenting styles , sibling relationships, household routines.
• School : Teacher-student interactions, peer relationships, classroom environment.
• Peer groups : Friendship dynamics, social support networks, peer pressure.
• Religious institutions : Belief systems, community engagement, moral teachings .
• Family-school : Parent-teacher communication, involvement in school activities.
• School-peer groups : Peer influence on academic performance, social dynamics within school settings.
• Family-religious institutions : Religious practices within the family, involvement in religious community activities.
• Peer groups-community services : Peer support for accessing community resources, involvement in community service projects.
• Parent’s workplace : Work hours, job stability, workplace culture.
• Community services : Access to healthcare, availability of recreational facilities, quality of public transportation.
• Mass media : Portrayal of societal norms, the influence of media on attitudes and behaviors.
• Extended family : Support from extended family members, family gatherings, and traditions.

Macrosystem

• Cultural norms : Attitudes toward education, gender roles, and family structure.
• Socioeconomic systems : Economic inequality, access to resources and opportunities.
• Political ideologies : Government healthcare, education, and social welfare policies.
• Historical context : Societal changes over time, impact of historical events on cultural values.

Chronosystem

• Family changes : Divorce, remarriage, birth of siblings.
• Socioeconomic transitions : Job loss, career advancement, changes in income level.
• Technological advancements : Impact of technology on communication patterns, learning opportunities, and social interactions.
• Historical events : Wars, economic recessions, civil rights movements.

These examples illustrate the diverse aspects within each system of ecological theory and highlight the interconnectedness of different environmental influences on human development.

How These Systems Interact

These systems within Bronfenbrenner’s ecological theory interact dynamically, influencing each other and ultimately shaping individual development. Here are a couple of examples to illustrate this interaction:

Microsystem-Mesosystem Interaction

Parental involvement in school activities can positively impact a child’s academic performance. When parents communicate with teachers (microsystem) and participate in school events (mesosystem), they reinforce the importance of education and create a supportive learning environment for the child.

Exosystem-Macrosystem Interaction

Government policies regarding parental leave can affect both family dynamics and workplace culture. When a country implements policies that support parental leave (macrosystem), it enables parents to spend more time with their children during critical developmental stages. 

This can lead to positive outcomes for children’s socioemotional well-being and family cohesion (exosystem). Additionally, such policies may contribute to broader societal changes by promoting gender equality in the workforce.

Practical Applications for Ecological Theory

Ecological theory offers valuable insights that have been applied across various fields, including psychology, education, social work, and public policy. Some key applications include:

Education and School Systems

• Understanding how different factors within and outside the classroom influence students’ academic achievement and socioemotional well-being.
• Designing interventions and programs to create supportive learning environments.
• Enhancing teacher-student relationships and peer dynamics.

Family Interventions and Counseling

• Assessing family dynamics and interactions using a holistic approach.
• Identifying areas for intervention to strengthen family functioning and relationships.
• Exploring connections between the family and other settings, such as school or community services.

Community Development and Social Services

• Addressing systemic barriers to opportunity and promoting community resilience.
• Designing culturally responsive interventions that meet the diverse needs of communities.
• Advocating for policies that promote social justice and equity.

Policy-Making and Advocacy

• Creating inclusive policies that support the well-being of all individuals and communities.
• Adapting policies to evolving societal needs and challenges.
• Recognizing the impact of institutional factors such as racism and economic inequality.

Research and Evaluation

• Studying the complex interactions between individuals and their environments.
• Identifying risk and protective factors that influence human development.
• Assessing interventions’ impact on multiple levels of the ecological hierarchy.

Ecological theory informs various fields, providing a comprehensive framework for understanding and promoting human development in many different contexts. Health practitioners, mental health professionals, policymakers, and researchers can utilize this framework collaboratively to create supportive environments and foster positive outcomes for all.

Strengths and Limitations of Ecological Theory

Bronfenbrenner’s ecological systems theory is one way of thinking about human development . Like other theories, it has both strengths and shortcomings.

• Comprehensive approach : Ecological theory provides a comprehensive framework for understanding human development by considering the complex interactions between individuals and their environments.
• Holistic approach : It emphasizes the importance of examining multiple levels of environmental influence, from immediate settings to broader societal contexts, to gain a holistic understanding of development.
• Applicability : The theory has practical applications across various fields, including education, social work, and policy-making, guiding interventions and programs aimed at promoting positive outcomes for individuals and communities.
• Emphasis on context : By highlighting the significance of environmental context, ecological theory acknowledges the diversity of human experiences and the impact of cultural, socioeconomic, and historical factors on development.

Limitations

• Complexity : The interconnected nature of ecological systems can make it challenging to disentangle the specific influences on individual development, leading to complexity in research and intervention efforts.
• Overlooks internal factors : Ecological theory primarily focuses on environmental influences on development, sometimes overlooking the role of individual agency and internal factors in shaping behavior and outcomes.
• Difficulty in generalization : Contextual factors vary widely across individuals and communities, making it difficult to generalize findings or interventions derived from ecological theory to different cultural or socioeconomic contexts.
• Potential for oversimplification : In attempting to capture the complexity of human development within a hierarchical framework, there is a risk of oversimplification, overlooking nuances and interconnections between systems.

While ecological theory offers valuable insights into the dynamic interplay between individuals and their environments, researchers and practitioners must be mindful of its limitations and consider them when applying the theory to real-world contexts.

Eriksson, M., Ghazinour, M. & Hammarström, A. Different uses of Bronfenbrenner’s ecological theory in public mental health research: what is their value for guiding public mental health policy and practice ? Soc Theory Health , 16, 414–433 (2018). https://doi.org/10.1057/s41285-018-0065-6

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Özdoğru, A. (2011). Bronfenbrenner’s ecological theory . In: Goldstein, S., Naglieri, J.A. (eds) Encyclopedia of Child Behavior and Development . Springer, Boston, MA. https://doi.org/10.1007/978-0-387-79061-9_940

Teater, B. (2021). Ecological systems theory . In K. W. Bolton, J. C. Hall, & P. Lehmann (Eds.), Theoretical Perspectives for Direct Social Work Practice (4th ed.). Springer Publishing Company. https://doi.org/10.1891/9780826165565.0003

Bronfenbrenner’s Ecological Systems Theory

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Learn about our Editorial Process

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

On This Page:

Bronfenbrenner’s ecological systems theory posits that an individual’s development is influenced by a series of interconnected environmental systems, ranging from the immediate surroundings (e.g., family) to broad societal structures (e.g., culture).

These systems include the microsystem, mesosystem, exosystem, macrosystem, and chronosystem, each representing different levels of environmental influences on an individual’s growth and behavior.

Key Takeaways

• Bronfenbrenner’s ecological systems theory views child development as a complex system of relationships affected by multiple levels of the surrounding environment, from immediate family and school settings to broad cultural values, laws, and customs.
• To study a child’s development, we must look at the child and their immediate environment and the interaction of the larger environment.
• Bronfenbrenner divided the person’s environment into five different systems: the microsystem, the mesosystem, the exosystem, the macrosystem, and the chronosystem.
• The microsystem is the most influential level of the ecological systems theory. This is the most immediate environmental setting containing the developing child, such as family and school.
• Bronfenbrenner’s ecological systems theory has implications for educational practice.

The Five Ecological Systems

Bronfenbrenner (1977) suggested that the child’s environment is a nested arrangement of structures, each contained within the next. He organized them in order of how much of an impact they have on a child.

He named these structures the microsystem, mesosystem, exosystem, macrosystem and the chronosystem.

Because the five systems are interrelated, the influence of one system on a child’s development depends on its relationship with the others.

1. The Microsystem

The microsystem is the first level of Bronfenbrenner’s theory and is the things that have direct contact with the child in their immediate environment.

It includes the child’s most immediate relationships and environments. For example, a child’s parents, siblings, classmates, teachers, and neighbors would be part of their microsystem.

Relationships in a microsystem are bi-directional, meaning other people can influence the child in their environment and change other people’s beliefs and actions. The interactions the child has with these people and environments directly impact development.

For instance, supportive parents who read to their child and provide educational activities may positively influence cognitive and language skills. Or children with friends who bully them at school might develop self-esteem issues. The child is not just a passive recipient but an active contributor in these bidirectional interactions.

2. The Mesosystem

The mesosystem is where a person’s individual microsystems do not function independently but are interconnected and assert influence upon one another.

The mesosystem involves interactions between different microsystems in the child’s life. For example, open communication between a child’s parents and teachers provides consistency across both environments.

However, conflict between these microsystems, like parents and teachers blaming each other for a child’s poor grades, creates tension that negatively impacts the child.

The mesosystem can also involve interactions between peers and family. If a child’s friends use drugs, this may introduce substance use into the family microsystem. Or if siblings do not get along, this can spill over to peer relationships.

3. The Exosystem

The exosystem is a component of the ecological systems theory developed by Urie Bronfenbrenner in the 1970s.

It incorporates other formal and informal social structures. While not directly interacting with the child, the exosystem still influences the microsystems. 

For instance, a parent’s stressful job and work schedule affects their availability, resources, and mood at home with their child. Local school board decisions about funding and programs impact the quality of education the child receives.

Even broader influences like government policies, mass media, and community resources shape the child’s microsystems.

For example, cuts to arts funding at school could limit a child’s exposure to music and art enrichment. Or a library bond could improve educational resources in the child’s community. The child does not directly interact with these structures, but they shape their microsystems.

4. The Macrosystem

The macrosystem focuses on how cultural elements affect a child’s development, consisting of cultural ideologies, attitudes, and social conditions that children are immersed in.

The macrosystem differs from the previous ecosystems as it does not refer to the specific environments of one developing child but the already established society and culture in which the child is developing.

Beliefs about gender roles, individualism, family structures, and social issues establish norms and values that permeate a child’s microsystems. For example, boys raised in patriarchal cultures might be socialized to assume domineering masculine roles.

Socioeconomic status also exerts macro-level influence – children from affluent families will likely have more educational advantages versus children raised in poverty.

Even within a common macrosystem, interpretations of norms differ – not all families from the same culture hold the same values or norms.

5. The Chronosystem

The fifth and final level of Bronfenbrenner’s ecological systems theory is known as the chronosystem.

The chronosystem relates to shifts and transitions over the child’s lifetime. These environmental changes can be predicted, like starting school, or unpredicted, like parental divorce or changing schools when parents relocate for work, which may cause stress.

Historical events also fall within the chronosystem, like how growing up during a recession may limit family resources or growing up during war versus peacetime also fall in this system.

As children get older and enter new environments, both physical and cognitive changes interact with shifting social expectations. For example, the challenges of puberty combined with transition to middle school impact self-esteem and academic performance.

Aging itself interacts with shifting social expectations over the lifespan within the chronosystem.

How children respond to expected and unexpected life transitions depends on the support of their ecological systems.

The Bioecological Model

It is important to note that Bronfenbrenner (1994) later revised his theory and instead named it the ‘Bioecological model’.

Bronfenbrenner became more concerned with the proximal development processes, meaning the enduring and persistent forms of interaction in the immediate environment.

His focus shifted from environmental influences to developmental processes individuals experience over time.

‘…development takes place through the process of progressively more complex reciprocal interactions between an active, evolving biopsychological human organism and the persons, objects, and symbols in its immediate external environment.’ (Bronfenbrenner, 1995).

Bronfenbrenner also suggested that to understand the effect of these proximal processes on development, we have to focus on the person, context, and developmental outcome, as these processes vary and affect people differently (Bronfenbrenner & Evans, 2000).

While his original ecological systems theory emphasized the role of environmental systems, his later bioecological model focused more closely on micro-level interactions.

The bioecological shift highlighted reciprocal processes between the actively evolving individual and their immediate settings. This represented an evolution in Bronfenbrenner’s thinking toward a more dynamic developmental process view.

However, the bioecological model still acknowledged the broader environmental systems from his original theory as an important contextual influence on proximal processes.

The bioecological focus on evolving person-environment interactions built upon the foundation of his ecological systems theory while bringing developmental processes to the forefront.

Classroom Application

The Ecological Systems Theory has been used to link psychological and educational theory to early educational curriculums and practice. The developing child is at the center of the theory, and all that occurs within and between the five ecological systems are done to benefit the child in the classroom.

• According to the theory, teachers and parents should maintain good communication with each other and work together to benefit the child and strengthen the development of the ecological systems in educational practice.
• Teachers should also be understanding of the situations their student’s families may be experiencing, including social and economic factors that are part of the various systems.
• According to the theory, if parents and teachers have a good relationship, this should positively shape the child’s development.
• Likewise, the child must be active in their learning, both academically and socially. They must collaborate with their peers and participate in meaningful learning experiences to enable positive development (Evans, 2012).

There are lots of studies that have investigated the effects of the school environment on students. Below are some examples:

Lippard, LA Paro, Rouse, and Crosby (2017) conducted a study to test Bronfenbrenner’s theory. They investigated the teacher-child relationships through teacher reports and classroom observations.

They found that these relationships were significantly related to children’s academic achievement and classroom behavior, suggesting that these relationships are important for children’s development and supports the Ecological Systems Theory.

Wilson et al. (2002) found that creating a positive school environment through a school ethos valuing diversity has a positive effect on students’ relationships within the school. Incorporating this kind of school ethos influences those within the developing child’s ecological systems.

Langford et al. (2014) found that whole-school approaches to the health curriculum can positively improve educational achievement and student well-being. Thus, the development of the students is being affected by the microsystems.

Critical Evaluation

Bronfenbrenner’s model quickly became very appealing and accepted as a useful framework for psychologists, sociologists, and teachers studying child development.

The Ecological Systems Theory provides a holistic approach that is inclusive of all the systems children and their families are involved in, accurately reflecting the dynamic nature of actual family relationships (Hayes & O’Toole, 2017).

Paat (2013) considers how Bronfenbrenner’s theory is useful when it comes to the development of immigrant children. They suggest that immigrant children’s experiences in the various ecological systems are likely to be shaped by their cultural differences. Understanding these children’s ecology can aid in strengthening social work service delivery for these children.

Limitations

A limitation of the Ecological Systems Theory is that there is limited research examining the mesosystems, mainly the interactions between neighborhoods and the family of the child (Leventhal & Brooks-Gunn, 2000). Therefore, the extent to which these systems can shape child development is unclear.

Another limitation of Bronfenbrenner’s theory is that it is difficult to empirically test the theory. The studies investigating the ecological systems may establish an effect, but they cannot establish whether the systems directly cause such effects.

Furthermore, this theory can lead to assumptions that those who do not have strong and positive ecological systems lack in development. Whilst this may be true in some cases, many people can still develop into well-rounded individuals without positive influences from their ecological systems.

For instance, it is not true to say that all people who grow up in poverty-stricken areas of the world will develop negatively. Similarly, if a child’s teachers and parents do not get along, some children may not experience any negative effects if it does not concern them.

As a result, people need to avoid making broad assumptions about individuals using this theory.

How Relevant is Bronfenbrenner’s Theory in the 21st Century?

The world has greatly changed since this theory was introduced, so it’s important to consider whether Bronfenbrenner’s theory is still relevant today. 

Kelly and Coughlan (2019) used constructivist grounded theory analysis to develop a theoretical framework for youth mental health recovery and found that there were many links to Bronfenbrenner’s ecological systems theory in their own more recent theory.

Their theory suggested that the components of mental health recovery are embedded in the ‘ecological context of influential relationships,’ which fits in with Bronfenbrenner’s theory that the ecological systems of the young person, such as peers, family, and school, all help mental health development.

We should also consider whether Bronfenbrenner’s theory fits in with advanced technological advancements in the 21st century. It could be that the ecological systems are still valid but may expand over time to include new modern developments.

The exosystem of a child, for instance, could be expanded to consider influences from social media, video gaming, and other modern-day interactions within the ecological system.

Neo-ecological theory

Navarro & Tudge (2022) proposed the neo-ecological theory, an adaptation of the bioecological theory. Below are their main ideas for updating Bronfenbrenner’s theory to the technological age:

• Virtual microsystems should be added as a new type of microsystem to account for online interactions and activities. Virtual microsystems have unique features compared to physical microsystems, like availability, publicness, and asychnronicity.
• The macrosystem (cultural beliefs, values) is an important influence, as digital technology has enabled youth to participate more in creating youth culture and norms.
• Proximal processes, the engines of development, can now happen through complex interactions with both people and objects/symbols online. So, proximal processes in virtual microsystems need to be considered.

Urie Bronfenbrenner was born in Moscow, Russia, in 1917 and experienced turmoil in his home country as a child before immigrating to the United States at age 6.

Witnessing the difficulties faced by children during the unrest and rapid social change in Russia shaped his ideas about how environmental factors can influence child development.

Bronfenbrenner went on to earn a Ph.D. in developmental psychology from the University of Michigan in 1942.

At the time, most child psychology research involved lab experiments with children briefly interacting with strangers.

Bronfenbrenner criticized this approach as lacking ecological validity compared to real-world settings where children live and grow. For example, he cited Mary Ainsworth’s 1970 “Strange Situation” study , which observed infants with caregivers in a laboratory.

Bronfenbrenner argued that these unilateral lab studies failed to account for reciprocal influence between variables or the impact of broader environmental forces.

His work challenged the prevailing views by proposing that multiple aspects of a child’s life interact to influence development.

In the 1970s, drawing on foundations from theories by Vygotsky, Bandura, and others acknowledging environmental impact, Bronfenbrenner articulated his groundbreaking Ecological Systems Theory.

This framework mapped children’s development across layered environmental systems ranging from immediate settings like family to broad cultural values and historical context.

Bronfenbrenner’s ecological perspective represented a major shift in developmental psychology by emphasizing the role of environmental systems and broader social structures in human development.

The theory sparked enduring influence across many fields, including psychology, education, and social policy.

Frequently Asked Questions

What is the main contribution of bronfenbrenner’s theory.

The Ecological Systems Theory has contributed to our understanding that multiple levels influence an individual’s development rather than just individual traits or characteristics.

Bronfenbrenner contributed to the understanding that parent-child relationships do not occur in a vacuum but are embedded in larger structures.

Ultimately, this theory has contributed to a more holistic understanding of human development, and has influenced fields such as psychology, sociology, and education.

What could happen if a child’s microsystem breaks down?

If a child experiences conflict or neglect within their family, or bullying or rejection by their peers, their microsystem may break down. This can lead to a range of negative outcomes, such as decreased academic achievement, social isolation, and mental health issues.

Additionally, if the microsystem is not providing the necessary support and resources for the child’s development, it can hinder their ability to thrive and reach their full potential.

How can the Ecological System’s Theory explain peer pressure?

The ecological systems theory explains peer pressure as a result of the microsystem (immediate environment) and mesosystem (connections between environments) levels.

Peers provide a sense of belonging and validation in the microsystem, and when they engage in certain behaviors or hold certain beliefs, they may exert pressure on the child to conform. The mesosystem can also influence peer pressure, as conflicting messages and expectations from different environments can create pressure to conform.

Bronfenbrenner, U. (1974). Developmental research, public policy, and the ecology of childhood . Child development, 45 (1), 1-5.

Bronfenbrenner, U. (1977). Toward an experimental ecology of human development . American psychologist, 32 (7), 513.

Bronfenbrenner, U. (1995). Developmental ecology through space and time: A future perspective .

Bronfenbrenner, U., & Evans, G. W. (2000). Developmental science in the 21st century: Emerging questions, theoretical models, research designs and empirical findings . Social development, 9 (1), 115-125.

Bronfenbrenner, U., & Ceci, S. J. (1994). Nature-nurture reconceptualised: A bio-ecological model . Psychological Review, 10 (4), 568–586.

Hayes, N., O’Toole, L., & Halpenny, A. M. (2017). Introducing Bronfenbrenner: A guide for practitioners and students in early years education . Taylor & Francis.

Kelly, M., & Coughlan, B. (2019). A theory of youth mental health recovery from a parental perspective . Child and Adolescent Mental Health, 24 (2), 161-169.

Langford, R., Bonell, C. P., Jones, H. E., Pouliou, T., Murphy, S. M., Waters, E., Komro, A. A., Gibbs, L. F., Magnus, D. & Campbell, R. (2014). The WHO Health Promoting School framework for improving the health and well‐being of students and their academic achievement . Cochrane database of systematic reviews, (4) .

Leventhal, T., & Brooks-Gunn, J. (2000). The neighborhoods they live in: the effects of neighborhood residence on child and adolescent outcomes . Psychological Bulletin, 126 (2), 309.

Lippard, C. N., La Paro, K. M., Rouse, H. L., & Crosby, D. A. (2018, February). A closer look at teacher–child relationships and classroom emotional context in preschool . In Child & Youth Care Forum 47 (1), 1-21.

Navarro, J. L., & Tudge, J. R. (2022). Technologizing Bronfenbrenner: neo-ecological theory.  Current Psychology , 1-17.

Paat, Y. F. (2013). Working with immigrant children and their families: An application of Bronfenbrenner’s ecological systems theory . Journal of Human Behavior in the Social Environment, 23 (8), 954-966.

Rhodes, S. (2013).  Bronfenbrenner’s Ecological Theory  [PDF]. Retrieved from http://uoit.blackboard.com

Wilson, P., Atkinson, M., Hornby, G., Thompson, M., Cooper, M., Hooper, C. M., & Southall, A. (2002). Young minds in our schools-a guide for teachers and others working in schools . Year: YoungMinds (Jan 2004).

Further Information

Bronfenbrenner, U. (1974). Developmental research, public policy, and the ecology of childhood. Child Development, 45.

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Ecological Systems Theory

Ecological Systems Theory (EST), also known as human ecology, is an ecological/ system framework developed in 1979 by Urie Bronfenbrenner (Harkonen, 2007). Harkonen notes that this theory was influenced by Vygotsky’s socio-cultural theory and Lewin’s behaviorism theory. Bronfenbrenner’s research focused on the impact of social interaction on child development. Bronfenbrenner believed that a person’s development was influenced by everything in the surrounding environment and social interactions within it. EST emphasizes that children are shaped by their interaction with others and the context. The theory has four complex layers called systems, commonly used in research. At first, ecological theory was most used in psychological research; however, several studies have used it in other fields such as law, business, management, teaching and learning, and education.

Previous Studies

EST has been used in many different fields, however, commonly, it is used in health and psychology, especially in child development (e.g., Heather, 2016; Esolage, 2014; Matinello, 2020). For instance, Walker et al. (2019) used an EST framework to examine risk factors for overweight and obese children with disabilities. The study focused on how layers of an ecological system or environment can negatively affect children with special needs in terms of weight and obesity. They found that microsystem such as school, family home, and extracurricular activities can impact overall health through physical activities and food selectivity. Furthermore, the second layer, mesosystem (e.g., family dynamic and parental employment), also can lead to an increase in children’s weight because of a lack of money to buy nutritious food. In addition, children may be socially isolated and excluded in ways that cause stress, and their parents might use food to reinforce or comfort them. The third layer the study adopted was the macrosystem. For example, some cultures discriminate against children with disabilities so that they face more difficulty gaining access to health services.

In the field of language teaching, Mohammadabadi et al. (2019) researched factors influencing language teaching cognition. They used an ecological framework to explore the factors influencing language teachers at different levels. They adopted the four systems from Bronfenbrenner’s theory for studying the issue. This study found that the ecological systems affect language teaching.  For example, the microsystem included a direct influence on teachers’ immediate surroundings, such as facilities, emotional mood, teachers’ job satisfaction, and linguistic proficiency. The mesosystem defined interconnections between teachers’ collaboration and their prior learning experience. The exosystem included the teaching program and curriculum and teachers’ evaluation criteria, while the macrosystem addressed the government’s rules, culture, and religious beliefs. In other words, researchers use EST to guide the design of their studies and to interpret the results.

Model of EST

Concepts, Constructs, and Propositions

The four systems that Brofenbrenner proposed are constructed by roles, norm and rules (see Figure 1). The first system is the microsystem. The microsystem as the innermost system is defined as the most proximal setting in which a person is situated or where children directly interact face to face with others. This system includes the home and child-care (e.g., parents, teacher, and peers). The second is the mesosystem. The mesosystem is an interaction among two or more microsystems where children actively participate in a new setting; for instance, the relationship between the family and school teachers. The third is the exosystem. This system does not directly influence children, but it can affect the microsystem. The effect is indirect. However, it still may positively or negatively affect children’s development through the parent’s workplace, the neighborhood, and financial difficulties. The outermost system is the macrosystem. Like the exosystem, the macrosystem does not influence children directly; however, it can impact all the systems such as economic, social, and political systems. The influence of the macrosystem is reflected in how other systems, such as family, schools, and the neighborhood, function (Kitchen et al., 2019). These four systems construct the EST which considers their influences on child or human development.

Bronfenbrenner (cited in Harkonen, 2007) noted that those environments (contexts) could influence children’s development constructively or destructively. As the proposition, the system influences children or human development in many aspects, such as how they act and interact, their physical maturity, personal characteristics, health and growth, behavior, leadership skills, and others. At the end of the ecological system improvement phase, Bronfenbrenner also added time (the chronosystem) that focuses on socio-history or events associated with time (Schunk, 2016). In summary, the views of this ecological paradigm is that environment, social interaction, and time play essential roles in human development.

Using the Model

There are many possible ways to use the model as teachers and parents. For teaching purposes, teachers can use the model to create personalized learning experiences for students. The systems support teachers and school administrators to develop school environments that are suitable to students’ needs, characteristics, culture, and family background (Taylor & Gebre, 2016). Because the model focuses on the context (Schunk, 2016), teachers and school administration can use the model to increase students’ academic achievement and education attainment by involving parents and observing other contextual factors (e.g., students’ peers, extra-curricular activities, and neighbor) that may help or inhibit their learning.

Furthermore, the EST model can support parents to educate and guide their children. It can prompt parents to assist their children in choosing their friends and finding good neighborhoods and schools. Additionally, they can build close connections to teachers, so they know their children’ skills and abilities. By involving themselves in schools, parents can positively influence their children’s educational context (Hoover & Sandler, 1997).

For research purposes, researchers can test and modify or refine the EST proposition, or they can find additional ways to measure it. Researchers also can develop questionnaires from the components or concepts and construct of EST. Additionally, the four levels of EST can be used by researchers to frame qualitative, quantitative, and mixed research (Onwuegbuzie, et.al., 2013).

At first, EST was used in children’s development studies to describe their development in their early stages influenced by the person, social, and political systems. Currently, EST is broadly applied in many fields. Schools or educational institutions can use EST to improve students’ achievement and well-being. Interaction between the family, parents, teachers, community, and political system will determine students’ development outcomes.

Esolage, D. L. (2014). Ecological theory: Preventing youth bullying, aggression, and victimization.  Theory into Practice. 53 , 257–264.

Harkonen, U. (2007, October 17). The Bronfenbenner ecological system theory of human development. Scientific Articles of V International Conference PERSON.COLOR.NATURE.MUSIC , Daugavpils University, Latvia, 1 – 17.

Heather, M.F. (2016). An ecological approach to understanding delinquency of youth in foster care . Deviant Behavior, 37 (2), 139 – 150.

Hoover-Dempsey, K. V., & Sandler, H. M. (1997). Why do parents become involved in their children’s education? Review of Educational Research , 67(1), 3–42. https://doi.org/10.3102/00346543067001003

Kitchen, J. A, (list all authors in reference list) (2019). Advancing the use of ecological system theory in college students research: The ecological system interview tool.  Journal of College Students Development, 60  (4), 381-400.

Martinello, E. (2020). Applying the ecological system theory to better understanding and prevent child sexual abuse.  Sexuality and Culture, 24 , 326-344

Mohammadabadi, A., Ketabi, S., & Nejadansari, D. (2019). Factor influencing language teaching cognition.  Studies in Second Language Learning and Teaching. 9 (4), 657 – 680.

Onwuegbuzie, A.J., Collins, K.M.T., & Frels, R.K. (2013). Foreword. International Journal of Multiple Research Approaches, 7 (1), 2-8.

Schunk, D. H. (2016). Learning theory: An educational perspective .  Pearson.

Taylor, R. D., & Gebre, A. (2016). Teacher–student relationships and personalized learning: Implications of person and contextual variables. In M. Murphy, S. Redding, & J. Twyman (Eds.), Handbook on personalized learning for states, districts, and schools (pp. 205–220). Temple University, Center on Innovations in Learning.

Walker, M., Nixon, S., Haines. J., & McPherson, A.C. (2019). Examining risk factors for overweight and obesity in children with disabilities: A commentary on Bronfenbrenner’s ecological system framework. Developmental Neurorehabilitation, 22 (5), 359 – 364.

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• Published: 14 September 2020

A guide to ecosystem models and their environmental applications

• William L. Geary   ORCID: orcid.org/0000-0002-6520-689X 1 , 2 ,
• Michael Bode 3 ,
• Tim S. Doherty   ORCID: orcid.org/0000-0001-7745-0251 1   nAff10 ,
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• Dale G. Nimmo   ORCID: orcid.org/0000-0002-9814-1009 6 ,
• Ayesha I. T. Tulloch   ORCID: orcid.org/0000-0002-5866-1923 7 ,
• Vivitskaia J. D. Tulloch   ORCID: orcid.org/0000-0002-7673-3716 8 , 9 &
• Euan G. Ritchie 1  

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• Ecosystem ecology

Applied ecology has traditionally approached management problems through a simplified, single-species lens. Repeated failures of single-species management have led us to a new paradigm — managing at the ecosystem level. Ecosystem management involves a complex array of interacting organisms, processes and scientific disciplines. Accounting for interactions, feedback loops and dependencies between ecosystem components is therefore fundamental to understanding and managing ecosystems. We provide an overview of the main types of ecosystem models and their uses, and discuss challenges related to modelling complex ecological systems. Existing modelling approaches typically attempt to do one or more of the following: describe and disentangle ecosystem components and interactions; make predictions about future ecosystem states; and inform decision making by comparing alternative strategies and identifying important uncertainties. Modelling ecosystems is challenging, particularly when balancing the desire to represent many components of an ecosystem with the limitations of available data and the modelling objective. Explicitly considering different forms of uncertainty is therefore a primary concern. We provide some recommended strategies (such as ensemble ecosystem models and multi-model approaches) to aid the explicit consideration of uncertainty while also meeting the challenges of modelling ecosystems.

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Acknowledgements

W.L.G. was supported by the Department of Environment, Land, Water and Planning Victoria, and by Parks Victoria. T.S.D. was supported by an Alfred Deakin Post-doctoral Research Fellowship. D.G.N. was supported by an Australian Research Council Discovery Early Career Researcher Award. A.I.T.T. was supported by an Australian Research Council Discovery Early Career Researcher Award. Silhouettes used in the Box 1 and 2 figures are taken from Phylopic.

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Tim S. Doherty

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Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Burwood, Victoria, Australia

William L. Geary, Tim S. Doherty & Euan G. Ritchie

Biodiversity Division, Department of Environment, Land, Water and Planning, East Melbourne, Victoria, Australia

William L. Geary

School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia

Michael Bode

CSIRO Oceans and Atmosphere, CSIRO, Hobart, Tasmania, Australia

Elizabeth A. Fulton

Centre for Marine Socioecology, University of Tasmania, Hobart, Tasmania, Australia

School of Environmental Science, Institute for Land, Water and Society, Charles Sturt University, Albury, New South Wales, Australia

Dale G. Nimmo

School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia

Ayesha I. T. Tulloch

Australian Rivers Institute, Griffith University, Nathan, Queensland, Australia

Vivitskaia J. D. Tulloch

Department of Forest and Conservation Science, University of British Columbia, Vancouver, British Columbia, Canada

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W.L.G. and E.G.R. conceived the ideas for the paper. W.L.G. led the writing. V.J.D.T. wrote Box 2. M.B. constructed and ran the model for Box 3. W.L.G., M.B., T.S.D., E.A.F., D.G.N., A.I.T.T., V.J.D.T. and E.G.R. all contributed to developing schematics and writing the paper.

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Geary, W.L., Bode, M., Doherty, T.S. et al. A guide to ecosystem models and their environmental applications. Nat Ecol Evol 4 , 1459–1471 (2020). https://doi.org/10.1038/s41559-020-01298-8

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Literary Theory and Criticism

Home › Eco Criticism › Ecocriticism: An Essay

Ecocriticism: An Essay

By NASRULLAH MAMBROL on November 27, 2016 • ( 3 )

Ecocriticism is the study of literature and environment from an interdisciplinary point of view where all sciences come together to analyze the environment and brainstorm possible solutions for the correction of the contemporary environmental situation. Ecocriticism was officially heralded by the publication of two seminal works, both published in the mid-1990s: The Ecocriticism Reader , edited by Cheryll Glotfelty and Harold Fromm , and The Environmental Imagination, by Lawrence Buell.

Ecocriticism investigates the relation between humans and the natural world in literature. It deals with how environmental issues, cultural issues concerning the environment and attitudes towards nature are presented and analyzed. One of the main goals in ecocriticism is to study how individuals in society behave and react in relation to nature and ecological aspects. This form of criticism has gained a lot of attention during recent years due to higher social emphasis on environmental destruction and increased technology. It is hence a fresh way of analyzing and interpreting literary texts, which brings new dimensions to the field of literary and theoritical studies. Ecocriticism is an intentionally broad approach that is known by a number of other designations, including “green (cultural) studies”, “ecopoetics”, and “environmental literary criticism.”

Western thought has often held a more or less utilitarian attitude to nature —nature is for serving human needs. However, after the eighteenth century, there emerged many voices that demanded a revaluation of the relationship between man and environment, and man’s view of nature. Arne Naess , a Norwegian philosopher, developed the notion of “Deep Ecology” which emphasizes the basic interconnectedness of all life forms and natural features, and presents a symbiotic and holistic world-view rather than an anthropocentric one.

Earlier theories in literary and cultural studies focussed on issue of class, race, gender, region are criteria and “subjects”of critical analysis. The late twentieth century has woken up to a new threat: ecological disaster. The most important environmental problems that humankind faces as a whole are: nuclear war, depletion of valuable natural resources, population explosion, proliferation of exploitative technologies, conquest of space preliminary to using it as a garbage dump, pollution, extinction of species (though not a human problem) among others. In such a context, literary and cultural theory has begun to address the issue as a part of academic discourse. Numerous green movements have sprung up all over the world, and some have even gained representations in the governments.

Large scale debates over “dumping,” North versus South environmentalism (the necessary differences between the en-vironmentalism of the developed and technologically advanced richer nations—the North, and the poorer, subsistence environmentalism of the developing or “Third World”—the South). Donald Worster ‘s Nature’s Economy (1977) became a textbook for the study of ecological thought down the ages. The historian Arnold Toynbee recorded the effect of human civilisation upon the land and nature in his monumental, Mankind and Mother Earth (1976). Environmental issues and landscape use were also the concern of the Annales School of historians , especially Braudel and Febvre. The work of environmental historians has been pathbreaking too. Rich-ard Grove et al’s massive Nature and the Orient (1998), David Arnold and Ramachandra Guha’s Nature, Culture, Imperialism (1995) have been significant work in the environmental history of India and Southeast Asia. Ramachandra Guha is of course the most important environmental historian writing from India today.

Various versions of environmentalism developed.Deep ecology and ecofeminism were two important developments. These new ideas questioned the notion of “development” and “modernity,” and argued that all Western notions in science, philosophy, politics were “anthropocentric” (human-centred) and “androcentric”(Man/male-centred). Technology, medical science with its animal testing, the cosmetic and fashion industry all came in for scrutiny from environmentalists. Deep ecology, for instance, stressed on a “biocentric” view (as seen in the name of the environmentalist group, “ Earth First! !”).

Ecocriticism is the result of this new consciousness: that very soon, there will be nothing beautiful (or safe) in nature to discourse about, unless we are very careful.

Ecocritics ask questions such as: (1) How is nature represented in the novel/poem/play ? (2) What role does the physical-geographical setting play in the structure of the novel? (3) How do our metaphors of the land influence the way we treat it? That is, what is the link between pedagogic or creative practice and actual political, sociocultural and ethical behaviour towards the land and other non-human life forms? (4) How is science —in the form of genetic engineering, technologies of reproduction, sexualities—open to critical scrutiny terms of the effects of science upon the land?

The essential assumptions, ideas and methods of ecocritics may be summed up as follows. (1) Ecocritics believe that human culture is related to the physical world. (2) Ecocriticism assumes that all life forms are interlinked. Ecocriticism expands the notion of “the world” to include the entire ecosphere. (3) Moreover, there is a definite link between nature and culture, where the literary treatment, representation and “thematisation” of land and nature influence actions on the land. (4) Joseph Meeker in an early work, The Comedy of Survival: Studies in Literary Ecology (1972) used the term “literary ecology” to refer to “the study of biological themes and relationships which appear in literary works. It is simultaneously an attempt to discover what roles have been played by literature in the ecology of the human species.” (5) William Rueckert is believed to have coined the term “ecocriticism” in 1978, which he defines as “the application of ecology and ecological concepts to the study of literature.”

Source: Literary Theory Today,Pramod K Nair

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Categories: Eco Criticism

Tags: Annales School , Arne Naess , Arnold Toynbee , Cheryll Glotfelty , Deep Ecology , Earth First! , Ecocriticism , green studies , Harold Fromm , Literary Theory , Mankind and Mother Earth , Nature and the Orient , Nature's Economy , The Comedy of Survival: Studies in Literary Ecology , The Ecocriticism Reader , The Environmental Imagination

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Ecological Model and Dynamic Systems: Understanding Human Development

This essay about Bronfenbrenner’s Ecological Model and Dynamic System Theory elucidates the intricate interplay between environmental systems and individual development. Through Bronfenbrenner’s framework, which encompasses nested ecosystems from the micro to the macrosystem, and Dynamic System Theory’s emphasis on self-organization and feedback loops, a comprehensive understanding of human development emerges. The text illustrates how familial, social, cultural, and temporal factors shape developmental trajectories, highlighting the significance of interconnected influences. By integrating these theories, the essay unveils a holistic perspective on human development, emphasizing the dynamic nature of interactions between individuals and their environment. This synthesis enriches our comprehension of development and underscores the importance of holistic interventions to support positive outcomes.

How it works

Delving into the intricacies of human development unveils a tapestry woven from a myriad of threads, blending individual attributes with the multifaceted layers of the surrounding environment. Among the array of theories illuminating this complex phenomenon, Bronfenbrenner’s Ecological Model and Dynamic System Theory emerge as beacons guiding our understanding towards a holistic comprehension of human development.

Urie Bronfenbrenner, a luminary in developmental psychology, crafted the Ecological Model as a conceptual map delineating the interconnectedness of human experiences within environmental systems.

At its essence, Bronfenbrenner’s theory paints a portrait of nested ecosystems, spanning from the microsystem—the immediate familial and social milieu—to the macrosystem, encompassing cultural and societal influences. These systems, interlaced with the mesosystem and exosystem, encapsulate the myriad contexts shaping human development, each exerting its unique sway on an individual’s growth trajectory.

In the intimate realm of the microsystem, familial dynamics, peer interactions, and educational settings converge to sculpt the landscape of daily experiences. Here, the tender tendrils of influence intertwine, shaping beliefs, attitudes, and interpersonal relationships. For instance, the nurturing cocoon of family bonds and the formative crucible of peer interactions play pivotal roles in molding cognitive schemas and emotional resilience.

Venturing beyond the immediate horizon, the mesosystem unfurls, weaving together the disparate strands of microsystems into a cohesive tapestry of developmental influences. It is within this realm of interconnectedness that the synergy between familial and educational spheres, for instance, catalyzes cognitive development and socialization. Conversely, discordant notes in the mesosystem symphony may herald challenges in navigating the developmental journey.

As the concentric circles expand, the exosystem beckons—a realm where indirect influences cast their shadow upon the developmental landscape. Here, the tendrils of societal structures, economic dynamics, and community resources intertwine, shaping the contours of developmental opportunities and constraints. From the ripple effects of parental employment policies to the reverberations of community resources, the exosystem casts a far-reaching shadow upon individual development.

Eclipsing the micro and exo realms, the macrosystem looms large—a vast expanse encompassing cultural mores, societal norms, and ideological undercurrents. Embedded within this intricate web of cultural influences lie the blueprints of gender roles, educational paradigms, and ethnic identities—shaping the developmental trajectory through subtle yet profound nudges.

Moreover, Bronfenbrenner’s Ecological Model embraces the temporal dimension through the chronosystem—a dynamic canvas upon which historical events, life transitions, and socio-cultural shifts unfold. From the epochal waves of technological revolutions to the ebbs and flows of socio-political landscapes, the chronosystem paints a vivid tableau of temporal flux, etching its imprint upon the developmental narrative.

Complementing Bronfenbrenner’s framework, Dynamic System Theory adds a dynamic hue to the developmental canvas, illuminating the ever-evolving interplay between individuals and their environment. Embracing the ethos of self-organization, this theory unveils the emergent patterns and behaviors forged through the crucible of environmental interactions.

Central to Dynamic System Theory are the intricate feedback loops—engines propelling the perpetual dance between individuals and their milieu. Through these feedback loops, the echoes of environmental influences reverberate, shaping developmental trajectories and catalyzing emergent phenomena.

Furthermore, Dynamic System Theory unveils the enigmatic allure of attractors and bifurcation points—heralding the threshold moments where developmental trajectories diverge or converge. These pivotal junctures, akin to cosmic crossroads, beckon the flux of change, steering the developmental odyssey towards new horizons.

Integrating Bronfenbrenner’s Ecological Model with Dynamic System Theory unveils a kaleidoscopic vista of human development—one where the intricate tapestry of environmental influences intertwines with the dynamic currents of individual agency and adaptation. This synergistic alliance illuminates the nuanced interplay between context and process, enriching our understanding of human development and paving the path towards holistic interventions fostering positive developmental outcomes.

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1 Ecological Model

Greg Goines

Ecological Model

It is common for individuals to think their eating behaviors are strictly a personal matter, and the purpose of this textbook is to completely eliminate that notion. Behavior is influenced by more than just an individual’s perception and thoughts. When health professionals or researchers are trying to determine why individuals behave a certain way, they apply behavioral theories. Behavior is influenced by various factors, such as the individual’s relationships with others, community, job, school, and the laws that the government has put in place. One behavioral theory that encompasses all of these factors is the social-ecological model .

Intrapersonal/Individual Level

To break down the ecological model, think of throwing a rock into a lake. When the rock hits the water, concentric rings form in the water and they grow bigger the further they get away from where the rock hit. The closer the ring is to the rock, the more influence that area has on behavior change. The impact forms five rings around the rock that increase in size the further it gets away from the impact point. The first and smallest ring would be the intrapersonal level of the ecological model. This includes all the knowledge, attitudes, skills, and beliefs that each individual possesses (Healthy Campus, n.d. ). This can also include gender, economic status, age, race, ethnicity, education, genetics, and other factors that impact how individuals view themselves. Barriers that exist at this level are considered personal, for example in a study observing the barriers that prevent older adults from being physically active one of the most commonly noted barriers was physical limitations (Bethancourt et al., 2014). This is the most personal ring but the other rings are just as important.

Interpersonal Level

The second smallest ring would be considered the interpersonal level.  A person’s social networks, such as family, friends, and coworkers, would be included in this level (Healthy Campus, n.d.). The people that individuals surround themselves with may influence them whether they are aware of it or not. For example, most children aren’t able to provide their own food, so their eating habits are a reflection of those that provide the food for them (Lynch & Batal, 2011). Most celebrations, i.e. a wedding, birthday party, or Thanksgiving dinner, cause the participants to eat according to the social occasion, not the level of current hunger. Today’s culture strongly associates the over-consumption of food with celebrations (Lynch & Batal, 2011). Even though eating is an individual choice, social and environmental cues can affect one’s behavior.

Community Level

Next, the third and middle ring is the community level. A community includes neighborhoods, nearby businesses, the built environment, and local infrastructure. In some scenarios, an individual wants to change but their community doesn’t enable that change due to lack of availability and safety concerns. In a study looking at the barriers that prevent children in low-income areas from being active, they had the children take pictures of their barriers. The results showed that many of the barriers occurred at the community level, such as not having a sidewalk to walk on or recreational areas not being maintained (Nichols et al., 2016). Changing behavior is hard to do when the environment around an individual does not provide the necessary resources.

Institutional Level

The fourth-largest ring would be the institutional level. This level includes jobs, schools, or organizations.  The decisions that organizations and schools make can dictate what their members can or cannot do and is out of an individual’s control.  Therefore, the results may positively and negatively impact different groups.  For example, school districts may decide to cut physical education from the curriculum. Nearly ten percent of middle schools in the US provided the recommended amount of daily, or weekly physical activity in school, which is sixty minutes or 225 minutes per week (Erfle & Gamble, 2015). This means that the majority of the time the students spend at the school is spent being sedentary. Lack of physical activity is strongly correlated with weight gain (Erfle & Gamble, 2015). Overweight children are more likely to be obese adults and have an increased risk of developing health conditions (Erfle & Gamble, 2015). Schools are supposed to equip the students with tools to be successful in life, but by limiting the amount of physical activity in school, they are putting students at a lifelong disadvantage.

Policy/ Government Level

Lastly, the largest and outermost ring would be the policy level. This level includes local, state, national, and global laws. Elected officials make decisions that can either provide aid in an individual’s quest to change or inhibit one’s ability to change. Instances of government intervention can include regulations that provide clear and understandable food labels as well as requiring that restaurants provide accurate calculations of the number of calories in their foods.

As shown in the infographic below, all of these levels interact with each other and they can shape or create eating habits. So when a person decides to make a change, it is important to understand the impact of every level of the ecological model. Therefore, the individual should understand what resources may be available to them and also how to advocate for the resources they would like to see in their social and work communities. Understanding eating behaviors is important as it can aid in the prevention and/or treatment of all issues along the weight continuum from the extremely underweight through the grossly obese.

Review Questions

Bethancourt, H. J., Rosenberg, D. E., Beatty, T., & Arterburn, D. E. (2014). Barriers to and facilitators of physical activity program use among older adults. Clinical Medicine & Research , 12(1–2), 10–20. https://doi-org.libproxy.clemson.edu/10.3121/cmr.2013.1171.

Erfle, S. E., & Gamble, A. (2015). Effects of daily physical education on physical fitness and weight status in middle school adolescents. Journal of School Health , 85(1), 27–35. https://doi-org.libproxy.clemson.edu/10.1111/josh.12217

Haardörfer, R., Alcantara, I., Addison, A., Glanz, K., & Kegler, M. C. (2016). The impact of home, work, and church environments on fat intake over time among rural residents: a longitudinal observational study. B MC Public Health , 16(1), 1–12. https://doi-org.libproxy.clemson.edu/10.1186/s12889-016-2764-z

Healthy Campus. (n.d.). Ecological Model. American College Health Association . https://www.acha.org/HealthyCampus/HealthyCampus/Ecological_Model.aspx.

Lynch, M., & Batal, M. (2011). Factors influencing child care providers’ food and mealtime decisions: An ecological approach. Child Care in Practice , 17(2), 185–203. https://doi-org.libproxy.clemson.edu/10.1080/13575279.2010.541424.

Nichols, M., Nemeth, L. S. , Magwood, G., Odulana, A. & Newman, S. (2016). Exploring the contextual factors of adolescent obesity in an underserved population through photovoice. Family & Community Health , 39(4), 301–309. doi: 10.1097/FCH.0000000000000118.

A theory based framework for understanding, exploring, and addressing social determinants of health at many levels

https://www.healthyteennetwork.org/wp-content/uploads/2015/06/TipSheet_IncreasingOurImpactUsingSocial-EcologicalApproach.pdf

occurring within the individual mind or self

being, relating to, or involving relations between persons

a group of people with a common characteristic or interest living together within a larger society

an established organization or corporation (such as a bank or university) especially of a public character

a high-level overall plan embracing the general goals and acceptable procedures especially of a governmental body

An Ecological Approach to Obesity and Eating Disorders Copyright © 2020 by Greg Goines is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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Ecological Models to Deal with Diabetes in Medicine Essay

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Diabetes: A Brief Outline

Behavioral change programs used in overcoming obesity, using health belief model to deal with diabetes, legislation as an ecological framework, factors targeted by the ecological model to alleviate the problem of diabetes.

Health is defined by the World Health Organization as a state of complete physical, mental, and social wellbeing. It is not merely the absence of disease or infirmity. Factors that affect health can be divided into many categories. The current paper explores these elements using a case study of diabetes. The analysis is provided in the context of health belief model as the preferred approach to behavioral change.

The term “diabetes” is used to describe a group of metabolic diseases characterized by high levels of sugar in the blood. The condition is mainly brought about by low or inadequate production of insulin in the body. It can also be caused by the failure of body cells to respond properly to insulin (Brown, Dougherty, Garcia, Kouzekanani & Hanis, 2002). There are two major types of diabetes. They include diabetes mellitus type 1 and diabetes mellitus type 2.

The latter is also known as the “disease of affluent”. It is characterized by chronic and non-communicable diseases. Personal lifestyles and societal conditions associated with economic development are believed to be important risk factors associated with this condition. Obesity is a growing health concern in the world, especially due to its high prevalence rates among the youth. For example, between 2001 and 2009, cases of this condition among individuals aged below 20 years rose by 23% in the USA alone (Cash, 2014).

Selected Model

The health belief model was selected for the purposes of this study to deal with diabetes. It is a psychological framework used to identify, predict, and explain behavioral patterns related to health (Carpenter, 2010). It has 4 major constructs highlighting the perceived threat and the associated benefits. They include the following:

• Perceived vulnerability.
• Apparent severity.
• Perceived benefits.
• Perceived hurdles.

The framework above was selected for a number of reasons. For example, it helps the target population to acknowledge the negative impacts of a given health condition (Marks, 2003). Behavioral change projects in relation to diabetes have progressed over the years. Today, they include a wide range of activities and approaches. Most of these interventions focus on the individual as the locus of change (Glanz, Rimer & Viswanath, 2008).

Behaviors Contributing to the Diabetes Problem

There are various individual behavioral elements that increase the risk of becoming diabetic or succumbing to the condition. They include, among others, unhealthy eating, lack of exercise, failure to take medication, and lack of problem solving skills (Cash, 2014). Other risk factors include lack of awareness and ignorance among the target population. It is important to address these issues to help deal with the health problem.

The model is made up of four interdependent elements as described below:

Perceived vulnerability

When using this model, individuals are made to realize that they can contract a given health condition if they fail to maintain a healthy lifestyle. The practitioner starts by first gauging the beliefs of the target population with regards to the link between risky lifestyle and diabetes. They are taught how poor feeding habits, lack of physical exercise, and such other issues may predispose them to diabetes (Carpenter, 2010). As such, they are encouraged to go for screening (Brown et al., 2002).

Apparent severity

Diabetes leads to a number of health complications. The aim here is to deal with the community’s belief systems in relation to perceived severity of diabetes. Members of the public will be made to acknowledge the consequences of contracting diabetes. As a result, they will realize that it is important to go for screening (Carpenter, 2010).

The aim is to help those at risk of suffering from type 2 diabetes to significantly reduce the chances of contracting it. In addition, the approach can be used to help those with diabetes to regulate their glycerin levels (Glanz et al., 2008).

Perceived benefits

The objective is to analyze the perceptions of the community members in relation to the avoidance of the risk factors (Marks, 2003). The benefits of adhering to the instructions provided will be made apparent.

Perceived barriers

Adhering to the instructions provided to help avoid or mitigate effects of diabetes has its consequences. One of them includes disruption of lifestyles (Kapyla, 1996). The beliefs of the target population in relation to this element will be gauged. Their fears will be dealt with accordingly.

The Ecological Determinants of Diabetes

Individual behavior is influenced to a large extent by their environment. The proposed ecological framework can be used to deal with factors beyond the control of the person at three levels.

Micro level

It contains structures that the individual has direct contact with (Kapyla 1996). They include the family, the school, and neighborhood. Diabetes can easily arise at this level because lifestyle is largely determined by significant others around the individual.

Legislations can be put in place to ensure that parents become good role models. In addition, access to unhealthy foods among children, such as sweetened drinks, should be regulated. Policies can also be formulated to ensure that schools organize walks and other forms of physical exercises for their learners (Cash, 2014).

The level connects two or more systems in which an individual lives. An example is the link between teachers and parents in relation to children with diabetes. Governments can formulate policies to rally people around activities related to prevention of diabetes in municipalities, districts, and healthcare. Persons living with diabetes should be accommodated at this level (Marks, 2003).

Macro level

It is made up of cultural values, customs, and laws (Kapyla 1996). Companies dealing with the manufacture of unhealthy foods should be banned from promoting their products near schools. In addition, government can set aside funds to support such projects as riding bicycle to school or work.

The condition can be controlled through physical exercise (Brown et al., 2002). To this end, one factor to be addressed entails the design of public structures. In such cases, people are likely to use the stairs instead of lifts. Another factor involves public transport. Sidewalks and bicycle lanes should also be properly maintained.

They should be regarded to be as important as highways (Kapyla 1996). The third element involves participation. Community leaders should ensure that food outlets within their jurisdictions adhere to set health standards. They should regulate the number of fast food joints to promote good eating habits. The last factor has to do with resources. Funds should be made available for projects aimed at reducing diabetes.

Behavior change is an effective way of controlling most health conditions, such as diabetes. The health belief model can be used to help change the perceptions of the target population in relation to diabetes.

However, the individual has no control over a number of elements relating to the environment around them. As such, there is a need to come up with an ecological model to complement the selected behavioral framework. In conclusion, it is clear that many diseases like diabetes can be prevented through the adoption of the appropriate interventions.

Brown, S., Dougherty, J., Garcia, A., Kouzekanani, K., & Hanis, C. (2002). Culturally competent diabetes self-management education for Mexican Americans: The Starr County border health initiative. Diabetes Care, 25 (2), 259-268.

Carpenter, C. (2010). A meta-analysis of the effectiveness of health belief model variables in predicting behavior. Health Communication, 25 (8), 661-669.

Cash, J. (2014). Family practice guidelines (3rd ed.). New York: Springer.

Glanz, K., Rimer, B., & Viswanath, K. (2008). Health behavior and health education: Theory, research, and practice (4th ed.). San Francisco, CA: Jossey-Bass.

Kapyla, M. (1996). Cultural-Ecological frame of reference as organizer of contents in environmental education. European Education, 28 (3), 82-94.

Marks, J. (2003). Perioperative management of diabetes. American Family Physician, 67 (1), 93-100.

• Healthy Consequences of Fast Foods
• Perception of Diabetes in the Hispanic Population
• Diabetes: Discussion of the Disease
• The Effect of Physical, Social, and Health Variables on Diabetes
• Diabetic Education Program
• Diabetes mellitus Education and hemoglobin A1C level
• Illuminate Diabetes Event Design
• Diabetic Renal Disease
• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2020, April 2). Ecological Models to Deal with Diabetes in Medicine. https://ivypanda.com/essays/ecological-models-to-deal-with-diabetes-in-medicine/

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1. IvyPanda . "Ecological Models to Deal with Diabetes in Medicine." April 2, 2020. https://ivypanda.com/essays/ecological-models-to-deal-with-diabetes-in-medicine/.

Bibliography

IvyPanda . "Ecological Models to Deal with Diabetes in Medicine." April 2, 2020. https://ivypanda.com/essays/ecological-models-to-deal-with-diabetes-in-medicine/.

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Developing a socio-ecological model for community engagement in a health programme in an underserved urban area

Lizzie Caperon

1 Bradford Institute for Health Research, Bradford, United Kingdom

Fiona Saville

2 Better Start Bradford, Mayfield Centre, Bradford, United Kingdom

Associated Data

All relevant data are within the manuscript and its Supporting Information files.

Despite a recent increase in community engagement in health initiatives during the COVID-19 pandemic, health inequalities and health inequities remain a serious problem for society, often affecting those in underserved communities the most. Often individualised incentives such as payment for vaccinations have been used to increase involvement in health initiatives but evidence suggests that these do not always work and can be ineffective. This paper addresses the real world problem of a lack of involvement of communities in health programmes and subsequent health inequalities. Using data from nine workshops with community members evaluating a large community health programme, we develop a socio-ecological model [SEM] of influences on community engagement in health programmes to identify holistic and systemic barriers and enablers to such engagement. To date SEM has not been used to develop solutions to improve community engagement in health programmes. Such an approach holds the potential to look beyond individualised conceptualisations of behaviour and instead consider a multitude of social and cultural influences. This knowledge can then be used to develop multi-faceted and multi-layered solutions to tackle the barriers to community engagement in health programmes. Our SEM highlights the overarching importance of the socio-cultural environment in influencing community engagement. Within the socio-cultural environment were factors such as trust, social support and community mindedness. We also found that other factors affecting community engagement fall within individual, economic, technological, political and physical environments. Such factors include engagement in community organisation governance and processes, access to and ability to use technology and access to safe outdoor spaces. We propose further testing our socioecological model in other communities.

Introduction

The COVID-19 pandemic has led to a fundamental shift in approaches to community health [ 1 ]. There has been an increase in participation in health prevention, awareness raising and community-led health-based activities during COVID-19 with estimates of a million people volunteering to support the pandemic response in the UK [ 2 ] and mutual aid groups providing support to citizens in countries throughout the world [ 3 ]. Global health guidelines and other research has highlighted that such community participation in health is crucial [ 4 – 6 ], especially the inclusion of perspectives from diverse and harder to reach communities [ 7 ]. However, despite a recent increase in community engagement in health initiatives, health inequity and inequalities persist [ 8 , 9 ]. The pandemic has uncovered amplified systemic health and socioeconomic inequities, especially in the geographical area in West Yorkshire in which our study takes place [ 10 ]. The context of the COVID-19 pandemic has amplified the need to consider effective ways to improve community engagement in health programmes. During the COVID-19 pandemic, for example, vaccine hesitancy, particularly in underserved communities [ 6 , 11 – 14 ], has highlighted that resistance to participation in health initiatives is a significant problem, as it was before COVID-19 [ 15 ]. Common attempts to address health problems in community settings have involved individual incentives [ 16 , 17 ]; payment or compensation for receiving vaccine doses being some of the most recent and prevalent examples, which research has found has variable effects and in many cases does not work [ 18 , 19 ]. In contrast, broader community-led solutions are being increasingly seen as key to tackling health inequalities [ 9 ], and research has pointed to the importance of community-level factors in influencing health outcomes [ 20 ].

Socioecological systems theory acknowledges the central influence of beliefs and ideologies in society whilst accounting for the interconnections amongst individuals and uses a holistic socio-ecological lens with which to understand behaviour [ 21 ]. The first socio-ecological model [SEM] was first introduced to understand human development in the 1970s and was formalised as theory in the 1980s [ 22 ]. Since SEM was conceived in the 1970s, there have been many interpretations of SEM models to develop multilevel approaches to areas such as public health promotion, violence prevention, healthy college campuses, safe practice in primary care and bowel cancer prevention to name a few [ 23 – 26 ]. Furthermore, the Centres for Diseases and Prevention in the US who have adapted the SEM for health promotion efforts to include spheres of organisational, community and policy [ 27 ]. Therefore, adopting a socioecological lens to understand and explore community engagement in community health initiatives brings with it great potential to develop multi-level solutions which cross multiple influential environments and tap into the social determinants of health which so often drive people’s decision making and therefore tackle health inequalities [ 28 , 29 ]. Taking a socioecological approach allows us to improve interventions across multiple system levels which lead to solutions not only within the health system but also through across political, physical, socio-cultural and other structures in society. Socio-ecological models hold great value in considering the interaction of behaviours across multiple levels of influence and lead to multi-level suggestions for interventions to effectively influence behaviour [ 27 , 30 – 32 ]. Some socio-ecological models see cultural context as important in interventions [ 33 , 34 ]. The complex role played by context in the development of health problems is connected to the social [ 28 , 29 ] and structural determinants of health [ 9 ].

Though socioecological models have been developed to consider influences of lifestyle behaviours such as dietary behaviour [ 32 , 35 ] and on physical activity [ 36 ] and whilst one recent study looked at how SEM could be used to engage diverse populations in a research programme [ 37 ], to the best of our knowledge there has been no exploration to date of socioecological influences on community engagement in health programmes. The purpose of this paper is to develop a socioecological model of behavioural influences on community engagement in a community health programme. Our paper therefore addresses the real world problem of a lack of involvement of communities in health programmes and subsequent health inequalities. We also address the gap in the current knowledge by developing a socio-ecological model [SEM] of influences on community engagement in health programmes to identify systemic barriers and enablers to such engagement. Such an understanding holds the potential to look beyond individualised conceptualisations of behaviour and consider a multitude of social and cultural influences. This knowledge can then be used to develop multi-faceted and multi-layered solutions to tackle the barriers to community engagement in health programmes.

Community engagement has been found to be particularly effective in public health interventions for disadvantaged groups [ 38 ]. Our study takes place with a community in an economically underserved urban area within three electoral wards in which the Better Start Bradford programme operates in Bradford, in northern England. Bradford was hit particularly hard by the COVID-19 pandemic [ 10 ]. The wards BSB serves are considered amongst the most underserved in England and have populations made up of many different ethnicities [ 39 ]. Bowling and Barkerend is the one ward which BSB serves. It has a total population of 22,200, 11.2% of houses in the ward are overcrowded and 29.2% of the population are under 16. Life expectancy in the ward is lower than the district average [73.9 for men and 78.6 for women] and the ward is ranked 3 rd out of 30 in the District for the 2019 Index of multiple deprivation. 42.7% of the population in the ward are white and 32.9% are Pakistani, with the remaining 24.3% made up of a range of other ethnicities. The dominant religion in the ward is Muslim [45.8%] with Christian [29.7] and no religion [14.6%] second and third respectively [ 40 ]. Bradford Moor is the second ward BSB serves with a population of 21,310 it is similar in size to Bowling and Barkerend but more houses [17.3%] are overcrowded in the ward. Like Bowling and Barkerend, life expectancy in the ward is lower than the district average [74 for men and 80 for women]. Bradford Moor is ranked 4 out of 30 wards in the District for the index of multiple deprivation. Demographically, 63.9% of the population of the ward are Pakistani and 17.3% are White with the remainder a mixture of different ethnicities. 72.8% of the ward are Muslim, 13% Christian and the remaining 14.2% belong to religions or have no religion [ 41 ]. Little Horton is the final district BSB serves with a population of 23,340 it is the largest of the three wards. 14.1% of homes are overcrowded and like the other two wards; life expectancy is below the district average at 76.3% for men and 82.2 for men. Little Horton is ranked 2 nd of the 30 Wards in the District for the 2019 index of multiple deprivation. The majority of the population is Pakistani [48.5%] with 28.8% white. The main religion in the ward is Muslim [58%] with 24.7% Christian and the remaining 17.3% a mixture of other religions or with no religion [ 42 ].

The Better Start Bradford [BSB] Programme is funded by the National Lottery Community Fund over 10 years 2015–2025. The programme aims to improve the health outcomes of children by commissioning projects and services aimed at pregnant women and families with children under the age of 4. Engaging families with these projects and services, as well as with key programme messages promoting healthy child development, is therefore crucial to the success of the programme. The BSB programme includes community members in a range of governance roles and in engagement roles. The Family and Community Engagement [FACE] team are link workers employed by BSB to develop community partnerships, support parent led group activities and recruit volunteers to the BSB Programme Community champions are members of the community who promote the BSB programme activities. In October 2020 we set out to develop a logic model for community engagement in the BSB programme as part of the programme service design process. This co-production process involved running nine workshops with community members including pregnant women and parents of children under 4 which the BSB targets, FACE team members, health professionals, researchers and BSB staff. These workshops explored the barriers to the community engaging in the programme as well as enablers which increased community engagement in the programme. As we were co-producing the logic model for community engagement we started the notice common themes on the influences of behaviour [barriers and enablers] for community engagement coming out of the workshops. As a result we decided to put these themes together into a socio-ecological model to more systematically and holistically explain why people do or do not engage in community health programmes. Therefore, this theoretical paper documents the barriers and enablers to community engagement we gathered from the initial service design process. This paper does not provide an evaluation of participation in the BSB programme; rather it explores the barriers and enablers to community engagement through a socio-ecological lens, using the BSB programme as a case study. The research team from the research and evaluation arm of BSB, the Better Start Bradford Innovation Hub, that is the evaluation arm of the BSB programme located within the Bradford Institute for Health Research, led the interpretation of findings documented in this paper.

Participants

Our participants had all been recruited to take part in the service design co-production process to evaluate the BSB Programme. These participants had been identified through discussion with members of the BSB team, specifically the Family and Community Engagement [FACE] team. Following their initial involvement in a coproduced service design process, participants consented to us using their data in follow up research, which this study represents. Participants comprised of 10 community members, volunteers [parents in the lead panel members, community champions and community board members] and other stakeholders [ward officers and social prescribers] who represented a range of ethnicities and genders and lived in the BSB target area to take part in our nine workshop sessions. Pregnant women and parents with children under the age of 4, who are the target population for the BSB programme, were included in the service design process and a range of participants offered different perspectives. Also included in the workshops were seven BSB team members including FACE team members. These were included to ensure that trusted community workers with whom other community members were familiar, were present. This also ensured that community members felt at ease and comfortable in the workshops.

Recruitment and consent

FACE team members contacted all participants, either in person or by telephone and asked them if they would like to take part in service design process. Participants had the objectives of the service design explained to them by the BSB team and if they agreed to participate, written consent was obtained. All workshops were conducted online [according to COVID-19 restrictions] via ZOOM, a videoconferencing platform increasingly being used to conduct qualitative research [ 43 ]. Three community members attended all nine workshops, however many attended two or three of the workshops, and the composition of the group in the workshops changed every week. The total number of participants over all workshops was 25. To provide consistency for attendees, sessions took place at the same time weekly for 9 weeks. The service design process was part of ongoing community engagement activities which participants had volunteered to take part in as part of Better Start Bradford’s ongoing community work. No direct quotations were used from the sessions in data analysis or write up, and no data provided would identify participants who were anonymised at the point of data analysis. All participants in the workshops provided written consent for their data to be used and for findings to be published.

Workshop structure and guide

Workshops were initially thematically structured around the seven Scottish standards for community engagement [SSCE] which have been used in areas such as community planning and health and social care [ 44 ]. Workshops were conducted between November 2020 and March 2021. Nine workshops were conducted lasting 90 minutes each. In the first workshop the BSB research team discussed with participants that the sessions would aim to develop a community engagement strategy. Following the introductory workshop, seven subsequent workshops discussed each one of the SSCE in turn. The final workshop provided an opportunity for the research team to feedback findings and asked for feedback from community members. A workshop guide consisting of an agenda with questions that would be asked in the session was circulated prior to each workshop by the research team. Example workshop guides can be seen in S1 Table .

The process from which data in this article is derived was an exercise in service design which would go on to inform future service evaluation of community engagement in the Better Start Bradford programme. As such it did not require formal ethical review approval [HRA decision 60/88/81]. No identifying personal information was obtained or recorded for the purposes of research or any other use. All participants in the workshops provided written consent for their data to be used and for findings derived from the service design process to be published in this article.

Transcripts were taken from the workshops by two members of the research team. Following each workshop, the research team compared their transcriptions of the sessions to ensure accounts of the workshops were full and accurate. Framework analysis was used [ 45 ] and a matrix structure of key themes was developed based on the Scottish standards [see Table 1 ]. Data was analysed using above frameworks in Microsoft Excel and Nvivo. The data was then re-analysed along the socio-ecological themes we as researchers observed were emerging. Our themes were indexed systematically, a process which entailed comparison within and between themes. Our data analysis began with a priori codes from the socio-ecological model [SEM] literature. These codes were formed from the initial theory behind Brofenbrenner’s first socio-ecological model which were nesting circles that placed the individual in the centre surrounded by various influential systems [ 22 ]. The microsystem closest to the individual contains the strongest influences and incorporates the interactions and relationships of the immediate surroundings. The outer rings of the models or outer environments have traditionally represented environments which have interactive forces on the individual such as community contexts or social networks [ 46 ]. SEM models illustrate that health behaviours are affected by the interaction between the characteristics of the individual, community and physical, social and political components.

We present our results under each of these themes below.

Therefore our a priori codes drew on existing models, particularly models which place socio-cultural influences and community as an overarching macro influence on behaviours [ 35 ]. We began loosely therefore with three levels of codes–individual, intermediate and higher. However, as we began to code the data we discovered that specific environments were present within the intermediate and higher levels which were specific to the community engagement behaviour. Following this we began to formulate the dominant ‘influences’ or environments such as political, economic, physical and technological influences. Our analysis process was validated by discussing the analysis with the BSB team and research team.

Analysis of our data, both a priori and a posteriori, led to thematic representation under six environments; individual, political, physical, technological, economic and socio-cultural environments. Within each environment we coded specific aspects such as ‘time to take part in activities’ [individual] or ‘allocation of funding’ [economic]. We found that many codes overlapped several environments, as socio-ecological modelling encourages, showing multi-faceted influences on behaviour. The full list of codes and the environments these fit within can be found in S2 Table . Our list of levels of influence, themes and example codes can be found in Table 1 .

Individual environment influences on community engagement

Several individual factors were apparent in influencing community engagement. Examples of these included the individual’s ability to use technology which in turn influenced whether they could take part in online community engagement activities [CEA’s]. This factor overlaps with the socio-cultural, technological and economic influences on behaviour as often technological equipment and infrastructure were expensive for individuals to buy. If individuals did have access to these, sometimes their abilities to engage with the technological environment were limited by a lack of skills, time or technological awareness. Some community members also lacked the social support to learn or develop the abilities to use technology, and sometimes cultural or language barriers stood in the way of them accessing CEA’s. Other individual factors included the ability [often connected to time available] to take part in CEA’s or to be part of community engagement governance processes which could amplify their voices. This factor overlapped with the socio-cultural and political environments. Often social pressures, a lack of social support or cultural expectations led participants, particularly female community members, bearing the brunt of childcare responsibilities. This led to less time to engage with CEA’s. Many community members stated they had problems finding time to engage in CEA’s when juggling paid work, housework, childcare and other responsibilities. Furthermore, some community members lacked time to engage with BSB governance structures provided by the community action group and informal monthly meetings for community champions. This was connected with the support individual community members received in the form of training to allow them to take part in BSB governance structures and to gather and feed comments from their communities back up through existing governance structures. Those individuals who had time had access to training and therefore involvement in the governance structures. The opposite was the case for those individuals who did not have time to engage.

Economic environmental influences on community engagement

Several environmental influences affected community member’s engagement with the BSB programme. These included the economic ability of the individual to buy or own technological equipment or the infrastructure [Wi-Fi, mobile data] which enabled their engagement in online activities necessary during the COVID-19 pandemic. Further factors affecting engagement were community members’ perceptions that funding for the BSB programme was not being spent according to community needs, leading to reluctance to engage with the programme. Therefore community members wanted to ensure that funding was allocated to local organisations rather than to larger, less connected organisations that would leave the community when funding ended. Community members stated that if the funding processes within the BSB programme were more transparent and funding was made available to local initiatives, they would be more willing to participate in BSB activities.

Technological influences on community engagement

Many community members stated a preference for the development of a blended approach using social media channels, local radio and TV with face-to-face engagement to ensure maximum accessibility for as many people in the community as possible. Another technological influence was the provision of a range of new, accessible methods to improve engagement including, for example, the introduction of a new mobile application and podcast to reach maximum number of people in the community. Furthermore, many community members and BSB team members stated that a range of direct and indirect methods of communication using trusted sources increased community engagement. These methods included text message services, websites, newsletters and others, representing a range of different spaces. A further technological and socio-cultural influence on community engagement was the use of a range of visual forms of communication which those who did not have English as their first language could access such as the use of photographs on publicity, tiktok videos and other visual media.

Physical influences on community engagement

Participants listed several physical influences on CEA’s including the need to offer accessible venues and interpreters, and to reach a range of community members where they were through activities such as door-knocking at different times of the week to access those who worked as well as those at home during the day. Soft outcome activities were praised by participants as effective such as cook and eat sessions, healthy mum groups and walking groups around local urban spaces. A range of physical spaces were being provided for the community to gather informally in ‘safe spaces’ such as community buildings. Street parties and coffee mornings in targeted areas also used physical spaces well and tapped in the importance of social support and face-to-face contact with other community members so important within the socio-cultural environment. Successful CEA’s adapted provision to ensure engagement during COVID-19, making use of physical spaces with smaller group activities, such as 1-2-1 walks with community members and FACE team members. The social contact these activities provided and the opportunities to leave the home environment were very important to some community members showing the overlapping importance of physical and socio-cultural environments.

Political influences on community engagement

Political influences on community engagement fell within national, local and organisational [BSB programme] levels. Most factors discussed related to political aspects of the BSB organisation, such as governance processes. Several community members agreed that community board members had been given the opportunity to review BSB policies and take part in governance processes. It was agreed by participants that a range of community voices were represented in governance roles, examples included Roma, Refugees and Asylum seekers voices were invited to participate in the BSB partnership board. BSB team members stated that there were a range of opportunities to engage with organisations outside BSB and partners/services such as ward officers, social prescribers and parent champion groups. One community members asked for an accessible directory of support from BSB and another asked that where possible consistent, transparent information could be provided on funding criteria and other BSB process to foster greater trust from the community. This factor suggests trust, a sociocultural influence, was of central importance to CEA’s. Several community members requested improved transparency within the organisation to develop complaints procedures, disseminate the organisational structure and others to show the community where they fit within the organisation. Community members also requested the use of mechanisms to allow community members to feedback their opinions about the BSB programme as it developed. Some of these were already present such as the Parent Champion groups, community champions, Community Advisory Board and Partnership board. In addition to these mechanisms the BSB team suggested that the community reference group monitoring the community engagement strategy in the future could provide an excellent feedback channel and a range of other methods such as comments boxes and feedback forms could also be used. These could be used alongside informal feedback channels after CEA’s such as feedback from informal conversations during events. One community member requested that a clear communication strategy be improved by BSB to ensure that BSB explain how decisions are made to the community and where the power lies for decision-making. Furthermore, the community members requested involvement in project evaluations and BSB impact-measuring activities and that governance documents were provided in an accessible format to show key findings to community members. The FACE team stated that Project Engagement Forums offer monthly opportunities for community members to plan CEA’s with project partners. Community members stated that more opportunities to engage with planning CEA’s would be advantageous.

Many community members praised the opportunities they had been given by BSB to gain skills as a result of their involvement, this thereby overlapping with the individual environment. Conferences and training had provided opportunities to meet fellow volunteers and build rapport with community members, indicating influence in the socio-cultural environment. Community members and the BSB team acknowledged that more work was needed to reach hard to reach groups such as Eastern European, Roma and White British populations.

Socio-cultural influences on community engagement

The socio-cultural environment was the most overriding influences on community engagement and many of these referred to the community specifically. We interpret the socio-cultural environment to include social factors [social support, relationships] and cultural aspects [social and cultural norms, ethnicity, religion, language]. These reflect society’s values, influences and norms. Such societal and social factors greatly influenced CEA’s at all levels. Our participants stated that the development of relationships between the community and community workers was key, and a priority for BSB should be increasing the capacity of the BSB workforce and sustaining strong, trusted relationships with the community to deliver health interventions and engage the community with health services. Online provision of CEA’s during the COVID-19 pandemic offered ‘dip in and out’ options which expanded the reach of the BSB programme to some hard to reach groups. However, central to these was trust from the community that such activities were delivered in their language or by community members/BSB workers they already knew. Recruitment of community champions had taken place from a range of different cultural groups which was seen by community members as important to have their own ethnic and cultural groups represented and ‘link’ workers operating between them and the BSB organisation. Warm up informal gathering in the community led by trusted community members provided valuable social interactions and were successful in bringing community members together.

Community members stated that BSB activities allowed a range of voices to be included and forums aimed at underrepresented voices, such as Roma and Dad’s groups, allowed groups to self-design activities that were appropriate and culturally specific. Additionally, more general societal changes in attitudes towards community engagement had had an impact. Waves of community mindedness and increased motivation to take part in CEA’s and these had optimised community volunteering and involvement during the COVID-19 pandemic. This had led to greater enthusiasm in recent months to participate in CEA’s and collaborate with community champions and parent champion groups. Community members, supported by BSB team members stated the need to use a range of creative methods to reach hard to reach groups and establish initial contact with community members to develop much needed social support for them during the challenging COVID-19 pandemic. Some community members stated they liked the ‘test and learn’ approach taken by the BSB team and FACE team, especially during the COVID-19 pandemic, to test new engagement strategies and activities to see what was effective. FACE team workers also used ‘soft intelligence’ by encouraging conversations between BSB workers and the community to explore which CEA’s were working well. Such trusted social interactions were key to ensuring CEA’s were targeted, effective and contextually appropriate. All community members stated that the social support offered to them by the BSB team was key in helping them to feel part of the community, engaged in BSB activities and willing to come back to take part in other activities in the future. Relationships community members had developed with others in their ethnic or cultural groups [e.g. the Eastern European or Pakistani communities] which shared language or cultural norms/traditions, had increased their willingness to leave home and take part in for example antenatal classes, walking groups or English language classes. Therefore the socio-cultural environment appeared to be of overarching influence to our participants in whether they took part in CEA’s.

Our socioecological model [ Fig 1 ] indicates multiple layers of influence on community engagement. Our model’s value is in its visual representation of the influences on community engagement behaviour in health programmes, a visualisation which is as yet under explored in the literature. In our model we use the concept of the individual at the centre followed by intermediate environment which have an influence on the behaviour of the individual and finally an all-encompassing environment at a higher level [the outer circle] which is the most overarching influential environment on all behaviours at the individual and intermediate level.

The individual environment representing individuals within the community is in the centre [Grey], the first level of influence. The intermediate levels of influence are economic, political, technological and physical [light blue]. The overarching level of influence is the socio-cultural environment including the community [dark blue]. The environmental influences overlap as some factors influencing community engagement in the health programme are nested within multiple environments as is the nature of SEM theory.

Our model, constructed of six overlapping environments; individual, economic, technological, physical, political and socio-cultural, puts the socio-cultural environment on the outer ring meaning it influences all environments within it. We categorise socio-cultural environment, drawing on elements of the socio-cultural psychological approach [ 47 ]. This approach emphasises that cultural factors such as language, social norms and social structures can play a significant role in defining behaviours. We consider factors such as social norms [informal understandings that govern the behaviour of members of society [ 48 ]], social structures [e.g. family, communities] and cultural practices as playing an important role in behaviours. As discussed in the introduction to our setting, the location in which this study took place is diverse and multi-cultural with multiple cultural and social influences working on individual community members. The socio-cultural environment importantly incorporates ‘the community’ and what the community represents. Our findings have shown this to include social support, face-to-face interactions, and a wide range of cultural and social norms, ethnicities and social identities. This is illustrated by our findings showing the importance of reaching different ethnic groups such as Eastern Europeans and other social groups such as refugees and Dad’s.

Whilst previous ecological models consider social or cultural environmental factors to be important in influencing other forms of behaviour such as dietary behaviour and physical activity [ 32 , 35 , 49 , 50 ], our model explores the importance of the sociocultural environment on community engagement. Our findings state that socio-cultural influences must be considered to develop tangible, multi-faceted and contextually-appropriate ways to engage communities in health programmes. Our approach sits in contrast to individualised approaches to community health such as monetary incentives which only consider the individual [ 18 , 19 ] rather than the broader, wider, multi-faceted influences on individual behaviour.

Social support to develop skills, trust and relationships with community workers in BSB and fellow community members was shown to foster community engagement. Other research corroborates that socio-cultural determinants such as social wellbeing, trust and community identity have been important in community engagement [ 51 ]. We found that trusted sources of information were important to community members and informal consultation was the most effective when it was done by those figures such as FACE team members, or champions coming from similar ethnic or cultural backgrounds who were trusted by the community. Other research supports our findings, showing that trust is vital for establishing responsive mutual communication in community engagement [ 52 , 53 ]. Our findings suggest that informal consultation, as with all community engagement activities, must include translation into different languages and be culturally appropriate, supporting other research which has found socio-cultural context to be a key consideration in developing community interventions [ 35 , 54 ]. Such informal consultation and multi-lingual and multi-cultural activities by community organisations like BSB can lead to improved community readiness in specific minority groups, as has been found in Roma communities in Bradford [ 55 ] and other minority groups around the world [ 56 ].

The physical, political, economic and technological environments all influenced community engagement behaviour. The physical environment defined aspects such as physical spaces where community engagement took place, these included trusted community buildings as well as opportunities for physical face-to-face interactions such as door-knocking. Other research has found that communal spaces have positive effects on community engagement and can foster social capital and place attachment [ 57 ]. Within the political environment BSB organisational policies and governance structures, and how accessible they were to the community, were important in influencing behaviour. We found that political processes within the BSB organisation needed to be made more transparent and feedback channels made clearer and stronger for all in the community. These political influences were highly influenced by socio-cultural environmental factors including the need to be inclusive and allow hard to reach voices to be heard. Community agency in governance processes has been found to be key in improving engagement and community participation [ 58 ]. Furthermore, community engagement should reflect national democratic processes, with programmes such as BSB aiming to reflect democratic values in allowing community voices to drive and guide community engagement activities [ 59 ].

The economic environment, influenced by the sociocultural environment, and sometimes overlapping with political or technological environments, impacted upon some community engagement behaviour. These included community members’ request for BSB to ensure funding for CEA’s was allocated to local organisations in a sustainable way, and allow funding criteria to be made readily available for the community to scrutinise. Other factors, such as the economic status of individuals influenced the amount of time they had to spend on CEA’s and their ability to buy technological equipment that they might need to take part in virtual CEA’s during the COVID-19 pandemic. We found that community members without access to skills to use technology or the technology itself should not be excluded from CEA’s. These community members must also be catered for as digital inequalities are particularly apparent in ethnic minority groups and those from low socio-economic backgrounds [ 60 – 63 ]. We found that a range of methods were preferred in both physical and virtual/technological environments to generate maximum community engagement with visual forms of communication being important for those minority communities who may not have English as their first language. Our findings suggest that community engagement should consist of a dual offering adopting a blended approach including virtual and face-to-face offerings to cater for all community needs. Furthermore, our findings suggest that social support should be used within the community to develop programmes to improve technological skills and language abilities to allow for more effective community engagement [ 64 , 65 ].

The individual level of influence affected community engagement in some aspects, such as the individual’s language or technological skills, or time to spend outside the home on CEA’s. However, we found that all individual factors were greatly influenced by surrounding environments, most prevalently the socio-cultural environment. This suggests that individual actions should not be seen in terms of individual behaviour change and that individual capabilities and motivations are broadly influenced by multiple environments in society [ 20 , 27 , 30 – 32 , 35 , 56 ] with socio-cultural influences playing an overarching role [ 35 , 54 ]. Our findings further support socioecological systems theory acknowledging the central influence of beliefs and ideologies across society whilst considering the interconnections and dependencies amongst community members [ 66 ]. A whole communities approach allows us to consider the influences on the community across multiple levels and environments [ 66 – 68 ]. Our findings add to a growing body of literature which argue that community-led solutions can address the social and structural determinants of health [ 9 ].

The context of the COVID-19 pandemic has led to an increase in community mindedness and increased motivation demonstrated in Bradford to optimise community volunteering [ 69 ]. In the context of the COVID-19 pandemic, community engagement has become increasingly important as vaccine hesitancy becomes an issue amongst some communities needing tackling and strategies to engage hard to reach communities to address health inequalities is vital [ 70 , 71 ].

The strength of SEM’s is that they can be used to understand a range of factors which influence people’s behaviour. SEMs such as ours can be used to develop strategies which can span multiple environments. An example of this is identifying community members who lack technological skills and equipment to engage in certain community engagement activities, and then developing training, providing equipment and ensuring social support is given to develop individual’s technical skills and support them in their engagement in health programmes such as BSB. Such strategies cross technological, economic, socio-cultural and individual environments in our model to represent a complex intervention which considers multiple influences on behaviour.

In our study, we have used a case study, a valuable form of research [ 72 ], to develop a SEM which allows us to develop a more nuanced understanding of the reasons why communities may or may not engage with health programmes such as BSB. Using this knowledge we can begin to develop interventions and strategies which consider all the influences on the individual illustrated in our model to tackle lack of engagement in health programmes. Such strategies go beyond the individualised methods of incentivising individual behaviour and see community engagement in health programmes as multi-faceted and multi-factorial.

Our study is not without limitations. Our service design process only represented community engagement in three wards in one city in the UK. Our workshops included a limited number of community members, and some groups in the community were under or unrepresented. Due to COVID-19 restrictions our workshops were held virtually and could have excluded some members of the community who did not have access to the internet or necessary technologies to take part. We also acknowledge that there are limitations with relying on the data collected from the service design process. Our synthesis of the key themes which formed the environments in the socio-ecological model was based on data which was not explicitly designed to inform such a model. The questions asked and discussions had during the workshops were focused on the formation of a logic model for community engagement with BSB and not specifically on the formation of our socio-ecological model. Ideally we would conduct more workshops specifically aimed at further developing, fleshing out and strengthening the socio-ecological model we have proposed. We cannot generalise our findings for other geographical locations where communities may involve different community engagement behaviour with different environmental influences Nonetheless, we consider that putting together these insights into a socio-ecological model allowed us, and hopefully will allow others, to understand barriers and enablers to community engagement in a more systematic way. Further testing would be required to investigate whether the same principles apply to other geographical, ethnic and socio-economic groups.

Our study represents the first time that community engagement behaviour has been considered in the form of a socioecological model, exposing the potential of taking a socioecological lens on community engagement activities to develop multi-faceted solutions to tackle health inequalities. Our socioecological model represents an opportunity to visualise the influences on community engagement in an underserved urban area so that community engagement activities can be more effectively developed which consider multiple environmental influences. Central to future community engagement activities should be the acknowledgment and incorporation of the socio-cultural environment. Developing relationships with the community built on trust and around provision of social support are vital. Consideration of the impact of economic, technological, political, physical and individual influences are also important to ensure community engagement activities are multi-dimensional and consider social and structural determinants of health [ 9 ]. We therefore hope our insights will allow others to understand the barriers and enablers to community engagement in health in a more systematic way. We advocate testing our socioecological model with a range of different communities within the UK and globally, to explore to what extent our model is applicable in a range of different community contexts.

Supporting information

Acknowledgments.

This project was only possible because of the enthusiasm and commitment of the members of the Community Reference Group. We are grateful to all the participants, the Community Reference Group, the Better Start Bradford partnership and staff, BSB projects, health professionals and researchers who have helped to make this project possible.

Funding Statement

Bradford Institute for Health Research received funding for this service design process the Big Lottery Fund, UK ( https://www.tnlcommunityfund.org.uk/ ) as part of the A Better Start programme. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability

• PLoS One. 2022; 17(9): e0275092.

Decision Letter 0

PONE-D-21-26119Developing a socio-ecological model for community engagement in an ethnically diverse and deprived urban area; a coproduction evaluationPLOS ONE

Dear Dr. Caperon,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Two reviewers and I have reviewed your manuscript, and have identified several issues that should be addressed before it could be further considered for publication. I have pulled what I view are the primary reviewer concerns and my own and enumerated them here.

• It is difficult to evaluate how your study fits into existing knowledge. A more rigorous, thorough review of existing literature on community engagement behaviors, particularly from sociological literature and that of relevant applied fields, would be helpful.
• Some methodological details and constraints should be addressed. Namely, how were candidate participants identified in the first place? And what are the limitations associated with your reliance on workshops? Was the target population for the BSB programme omitted from your workshops for the very reasons they are constrained from CEA behaviors? Were the same 10 community members at all nine of the workshops, or a different 10 each time? If the same 10, how does this sample size or repeated engagement of the same people affect your findings, either for better or worse?
• A more detailed explanation is warranted of the ethnic, economic, and cultural backgrounds of the study areas and participant communities. Correspondingly, terminology such as “ethnically diverse” and “deprived”, if retained, should be defended as the accepted and appropriate terminology for the study community.
• It does not seem that this study really involves co-production. What was co-produced: the BSB programme itself, or your research about it? Or, perhaps neither is co-produced, and you might instead consider different terminology? The research methods reported in this manuscript do not indicate co-production.
• The BSB programme isn’t much described. Since this paper evaluates participation in the BSB programme, more information about the programme is warranted. While the programme seems to target children’s health, the manuscript doesn’t take up that theme after initially introducing it in the “setting” section.
• The manuscript claims to develop a model, but the emergence of the model from the research is unclear. The model appears to consist largely of circles containing the “theme” or “environment” labels. It is not annotated nor well-described. It is not evident how those themes or environments were arrived at. Were they established a priori, or did they emerge as an outcome of considering the workshop transcripts? Moreover, is the size, position, and nesting or overlap of the circles significant? What does their spatial organization in the diagram convey? And, what is the role or significance of the floating words (“language”, “Socio cultural norms”, etc.)? Please clarify what makes this diagram a socioecological “model” of community engagement behaviour, and how one would use the model.
• On a related note, table 1 is unuseful to the reader. It is not easily readable, and is largely redundant with material in the main text. Perhaps a more synthetic table with key take-home points could be made, and the table contents as they exist now be moved to supplemental information.
• The manuscript is riddled with small errors of grammar and punctuation.
• Please indicate the mechanism by which your data will be made available.

A heavily revised manuscript could be considered for possible publication if you are able to address these concerns.

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Reviewer #1: Remarks to the Author:

*It is important to note that I am an expert in network science for complex social systems and interest in social mobility and gathering. However, I’m not an expert on sociology.

A. Originality and the key results

As far as I know, it seems to be the first application of socio-ecological analysis to community engagement in an ethnically diverse and deprived urban area. The author introduces the socio-ecological model with multiple layers of influence on community engagement activities (CEA) in six environmental factors: individual, political, physical, technological, economic, and socio-cultural. Each factor is comprehensively analyzed and presented with its associated influence as well as its own influence on community engagement.

The author highlights the importance of the overall influences of the socio-cultural environment which include social support, trust, relationships in communities, and ethnicity or language. At the same time, the author presents multi-factored aspects needed to ensure community engagement activities such as time spent on CEA, transparency through democratic process, and access to technology or communal spaces.

Finally, the author suggests that considering multiple environmental influences and incorporation of the socio-cultural environment can be more effectively developed the community engagement activities.

B. Data and model coverage

The co-production process that has been analyzed in this manuscript only represents the communities within three electoral wards that are considered deprived and have diverse ethnic populations. Also, workshops have some limitations in the number of community members or the existence of under or unrepresented groups as mentioned in the manuscript.

Due to the specificity of these data, further detailed explanations of the ethnic, economic, and cultural backgrounds of the restricted areas or participated communities are needed to avoid the error of hasty generalization and to specify the scope of application of the model.

C. Suggested improvements

The framework analysis and developed matrix structure of key socio-ecological themes are adopted to analyze the data. Although framework analysis lists environments that influence community engagement, the evidence in the results is insufficiently descriptive so statistical analysis or regression methods are recommended to support the results.

Reviewer #2: This paper provides a general sociological look at the complexity of community engagement related to a health program, offering insights into the interconnected and multifaceted aspects of community life that might limit or enable engagement among diverse individuals. The use of community focus groups and workshops seems appropriate to the research questions. I have some concerns with the framing, novelty, contribution, and overall clarity of the article.

Overall, I found the framing of the methods to be confusing. Either the authors are doing a sociological evaluation of a “coproduced” program, or they are trying to co-produce research findings within a sociological model (but not really using any coproduction methods), but the objectives are unclear and mismatched with the methods and findings.

The introduction needs to provide a broader view of the need for this sort of work in terms of theoretical or methodological developments, criticism, evaluation, or novel contributions.

You are clearly applying a co-produced research method for good reason, but why this would be useful or informative to the journal’s audience is unconvincing. There is really limited discussion or nuance around the theory of co-production here, or health related co-production work. Further, the language you use to describe the community is so vague and at patronizing. Did the participants in this coproduced project think of themselves as “ethnically diverse and deprived”? Which ethnicities? Do sociologists use the term deprived? If so, do you have a citation? Or some quantitative data to inform us of the demographics and trends in this area relative to your terms?

It is unclear what sort of entity is running the project, relative to the community leadership and the various layers of governance and organizing that could be going on in the area. Is this a university? A local or national government? What role did the community have in security the funding, or setting project goals?

I would suggest finding an alternative to the term “stakeholder.”

I would suggest using direct language and taking up an active voice to more clearly indicate various actions and sources of agency and activity in your narrative.

How interesting that program design is not considered research and does not require ethics clearance. I understand this is a strange “no-researcher-land” where co-produced research is concerned, however I’m also concerned that you have not cited the large body of ethics research, particularly ethics for working with underserved communities of color in research, in your ethics section. You have not outlined efforts to provide information or reciprocity back to your community participants in any form. If the Scottish standards for workshops of this type provide some details here, those should be explicitly documented in the methods.

Your participant recruitment methods are very unlearn.

After reading your analysis methods, it is quite clear that this is not coproduction, but focus group research conducted by the research team using thematic analysis.

Did you perhaps also refer to the literature while conducting this analysis? If so, which theoretical body of work informed your assessment of the data? Do you have any citations to suggest evidence of how you enhanced the trustworthiness and validity, or relationship to theoretical grounding, in your work?

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Submitted filename: PONE-D-21-26119.docx

Author response to Decision Letter 0

20 Apr 2022

Our detailed response to reviewers is presented in a table in the attached document named 'Response to Reviewers'. Please find the text of that response below (without the table formatting). Many thanks.

Dear Plos One team,

Please find our response to reviewer and editor comments below.

Compiled editor comments Author response to compiled editor comments

1. It is difficult to evaluate how your study fits into existing knowledge. A more rigorous, thorough review of existing literature on community engagement behaviors, particularly from sociological literature and that of relevant applied fields, would be helpful. Thank you for this observation. On re-reading the introduction we acknowledge that our aims and grounding in the literature were not clear. We have undertaken a thorough re-writing of the introduction to better set our study within the literature, making it clear that we aim to explore how a socio-ecological model can explore more multi-faceted solutions to community engagement in health programmes.

We would like to thank reviewer 1 for their comments regarding the originality of the study, indicating that it is well placed to explore an underexplored area:

‘A. Originality and the key results

Finally, the author suggests that considering multiple environmental influences and incorporation of the socio-cultural environment can be more effectively developed the community engagement activities.’

As we now explain in the introduction we argue that socio-ecological modelling is the best conceptual tool to gather the common themes we were identifying from a service design process in our study area with underserved communities in Bradford, UK. For example, our introduction states:

‘By developing this socio-ecological model we hope that community engagement in health programmes can be understood more systematically and holistically considering wider social determinants, and therefore the barriers to community engagement in health programmes can be addressed in multifaceted ways which take into account the social determinants of health. Such an understanding holds the potential to look beyond individualised conceptualisations of behaviour but rather takes into account a multitude of social and cultural influences.’

2. Some methodological details and constraints should be addressed. Namely, how were candidate participants identified in the first place?

And what are the limitations associated with your reliance on workshops?

Was the target population for the BSB programme omitted from your workshops for the very reasons they are constrained from CEA behaviors?

Were the same 10 community members at all nine of the workshops, or a different 10 each time? If the same 10, how does this sample size or repeated engagement of the same people affect your findings, either for better or worse? Thank you for your observations – as a result of them we have realised that our methods were not clear and we apologise for this. We have described our methods much more clearly now. For example, in the introduction under ‘Setting’ we state that as we were co-producing the logic model for CE (community engagement) during workshops with community members as part of a separate, initial piece of research, we started to notice common themes on the influences of behaviour for community engagement. It was out of these observations that we decided to put these themes together into a socio-ecological model to more systematically and holistically explain why people do or do not engage in community health programmes:

‘ In October 2020 we set out to develop a logic model for community engagement in the BSB programme as part of the programme service design process. This co-production process involved running nine workshops with community members including pregnant women and parents of children under 4 which the BSB targets, FACE team members, health professionals, researchers and BSB staff. These workshops explored the barriers to the community engaging in the programme as well as enablers which increased community engagement in the programme. As we were co-producing the logic model for community engagement we started the notice common themes on the influences of behaviour for community engagement coming out of the workshops. As a result we decided to put these themes together into a socio-ecological model to more systematically and holistically explain why people do or do not engage in community health programmes. Therefore, this theoretical paper documents the results of a service evaluation which took place as part to review the existing service design of Community Engagement across the BSB programme at the midpoint of programme delivery.’

Therefore, participants were identified as part of the initial study. We have explained in the methods under ‘Participants’ how participants were identified for the initial study:

‘Our participants had all been recruited to take part in the service design co-production process to evaluate the BSB Programme. These participants had been identified through discussion with members of the BSB team, specifically the Family and Community Engagement [FACE] team...’

We have clarified in the ‘participants’ section that:

‘We ensured that we recruited pregnant women and parents with children under the age of 4 who are the target population for the BSB programme.’

We have edited the ‘recruitment and consent’ section to explain that:

‘Three community members attended all nine workshops, however many attended two or three of the workshops, and the composition of the group in the workshops changed every week. The total number of participants over all workshops was 25.’

We have explained in our limitations section (penultimate paragraph of the discussion) that:

‘Our service design process only represented community engagement in three wards in one city in the UK. Our workshops included a limited number of community members, and some groups in the community were under or unrepresented. Due to COVID-19 restrictions our workshops were held virtually and could have excluded some members of the community who did not have access to the internet or necessary technologies to take part. We cannot generalise our findings for other geographical locations where communities may involve different community engagement behaviour with different environmental influences. Further testing would be required to investigate whether the same principles apply to other geographical, ethnic and socio-economic groups.’

We have added to this section that we acknowledge the limitations of relying on the workshops for the formation of our socio-ecological model:

‘We also acknowledge that there are limitations with relying on the data collected from the service design process. Our synthesis of the key themes which formed the environments in the socio-ecological model was based on data which was not explicitly designed to inform such a model. The questions asked and discussions had during the workshops were focused on the formation of a logic model for community engagement with BSB and not specifically on the formation of our socio-ecological model. Ideally we would conduct more workshops specifically aimed at further developing, fleshing out and strengthening the socio-ecological model we have proposed. Nonetheless, we consider that putting together these insights into a socio-ecological model allowed us, and hopefully will allow others, to understand barriers and enablers to community engagement in a more systematic way.’

3. A more detailed explanation is warranted of the ethnic, economic, and cultural backgrounds of the study areas and participant communities. Correspondingly, terminology such as “ethnically diverse” and “deprived”, if retained, should be defended as the accepted and appropriate terminology for the study community. Thank you, we agree that a more detailed explanation of the backgrounds of the study areas was warranted. We have included a description of each of the three wards BSB serves in the Setting section:

‘Our study takes place with a community in an economically underserved urban area within three electoral wards in which the Better Start Bradford programme operates in Bradford, in northern England. Bradford was hit particularly hard by the COVID-19 pandemic (10). The wards BSB serves are considered amongst the most underserved in England and have populations made up of many different ethnicities (39). Bowling and Barkerend is the one ward which BSB serves. It has a total population of 22,200, 11.2% of houses in the ward are overcrowded and 29.2% of the population are under 16. Life expectancy in the ward is lower than the district average (73.9 for men and 78.6 for women) and the ward is ranked 3rd out of 30 in the District for the 2019 Index of multiple deprivation where 1 is the most deprived. 42.7% of the population in the ward are white and 32.9% are Pakistani, with the remaining 24.3% made up of a range of other ethnicities. The dominant religion in the ward is Muslim (45.8%) with Christian (29.7) and no religion (14.6%) second and third respectively (40). Bradford Moor is the second ward BSB serves with a population of 21,310 it is similar in size to Bowling and Barkerend but more houses (17.3%) are overcrowded in the ward. Like Bowling and Barkerend, life expectancy in the ward is lower than the district average (74 for men and 80 for women). Bradford Moor is ranked 4 out of 30 wards in the District for the index of multiple deprivation. Demographically, 63.9% of the population of the ward are Pakistani and 17.3% are White with the remainder a mixture of different ethnicities. 72.8% of the ward are Muslim, 13% Christian and the remaining 14.2% belong to religions or have no religion(41). Little Horton is the final district BSB serves with a population of 23,340 it is the largest of the three wards. 14.1% of homes are overcrowded and like the other two wards; life expectancy is below the district average at 76.3% for men and 82.2 for men. Little Horton is ranked 2nd of the 30 Wards in the District for the 2019 index of multiple deprivation. The majority of the population is Pakistani (48.5%) with 28.8% white. The main religion in the ward is Muslim (58%) with 24.7% Christian and the remaining 17.3% a mixture of other religions or with no religion (42).’

Thank you for your valuable observations regarding language, as researchers we must always be open to revising the language we use to describe populations. We have replaced all references to deprived with underserved and we have removed reference to ethnically diverse to describe our study population.

4. It does not seem that this study really involves co-production. What was co-produced: the BSB programme itself, or your research about it? Or, perhaps neither is co-produced, and you might instead consider different terminology? The research methods reported in this manuscript do not indicate co-production. From your comments it’s clear that our description of co-production was confusing, we apologise for this. We meant to explain that the logic model process from which our data was derived was co-produced. We have removed reference to co-production throughout the article to make it clearer except in relation to the logic model process.

We have explained in the setting section of the introduction that the co-production was only in relation to the logic model process and removed co-production from the title of this article.

5. The BSB programme isn’t much described. Since this paper evaluates participation in the BSB programme, more information about the programme is warranted. While the programme seems to target children’s health, the manuscript doesn’t take up that theme after initially introducing it in the “setting” section. Thank you we have provided more detail on the description of the BSB programme. We have now clarified that the paper doesn’t evaluate participation in the BSB programme. Rather it explores the barriers and enablers to community engagement, using the BSB programme as a case study.

‘The Better Start Bradford [BSB] Programme is funded by the National Lottery Community Fund over 10 years 2015-2025. The programme aims to improve the health outcomes of children by commissioning projects and services aimed at pregnant women and families with children under the age of 4. Engaging families with these projects and services, as well as with key programme messages promoting healthy child development, is therefore crucial to the success of the programme. The BSB programme includes community members in a range of governance roles and in engagement roles. The Family and Community Engagement (FACE) team are link workers employed by BSB to develop community partnerships, support parent led group activities and recruit volunteers to the BSB Programme Community champions are members of the community who promote the BSB programme activities.’

‘This paper does not provide an evaluation of participation in the BSB programme, rather it explores the barriers and enablers to community engagement in the BSB programme through a socio-ecological lens, using the BSB programme as a case study.’

We have provided detail on the role of the BSB programme in Bradford and have also added a more detailed explanation of what the logic model process (from which our data was derived) was aiming to achieve in the Setting section:

‘In October 2020 we set out to develop a logic model for community engagement in the BSB programme as part of the programme service design process. This co-production process involved running nine workshops with community members including pregnant women and parents of children under 4 which the BSB targets, FACE team members, health professionals, researchers and BSB staff. These workshops explored the barriers to the community engaging in the programme as well as enablers which increased community engagement in the programme. As we were co-producing the logic model for community engagement we started the notice common themes on the influences of behaviour for community engagement coming out of the workshops. As a result we decided to put these themes together into a socio-ecological model to more systematically and holistically explain why people do or do not engage in community health programmes.’

6. The manuscript claims to develop a model, but the emergence of the model from the research is unclear. The model appears to consist largely of circles containing the “theme” or “environment” labels. It is not annotated nor well-described. It is not evident how those themes or environments were arrived at. Were they established a priori, or did they emerge as an outcome of considering the workshop transcripts? Moreover, is the size, position, and nesting or overlap of the circles significant? What does their spatial organization in the diagram convey? And, what is the role or significance of the floating words (“language”, “Socio cultural norms”, etc.)? Please clarify what makes this diagram a socioecological “model” of community engagement behaviour, and how one would use the model. Thank you for this comment, we apologise for the lack of clarity with describing how the model emerged. We have now provided a much more detailed discussion of Socio-ecological models in the introduction to set context. We have also provided a detailed explanation of how the environments in the model were developed partially a priori and partially a posteriori in the results section. This explanation links to the socio-ecological literature:

‘Our data analysis began with a priori codes from the socio-ecological model [SEM] literature. These codes were formed from the initial theory behind Brofenbrenner’s first socio-ecological model which were nesting circles that placed the individual in the centre surrounded by various influencial systems (19). The microsystem closest to the individual contains the strongest influences and incorporates the interactions and relationships of the immediate surroundings. The outer rings of the models or outer environments have traditionally represented environments which have interactive forces on the individual such as community contexts or social networks(46). SEM models illustrate that health behaviours are affected by the interaction between the characteristics of the individual, community and physical, social and political components.

Therefore our a priori codes drew on existing models, particularly models which place socio-cultural influences and community as an overarching macro influence on behaviours (33). We began loosely therefore with three levels of codes – individual, intermediate and higher. However, as we began to code the data we discovered that specific environments were present within the intermediate and higher levels which were specific to the community engagement behaviour. Following this we began to formulate the dominant ‘influences’ or environments such as political, economic, physical and technological influences’

In this section we have now also provided a table which shows the levels of influence and themes which emerged from the analysis (Table 1). For clarity we have edited the SEM to remove any floating words and ensure that the levels of influence in the model are clearly labelled and described.

‘Analysis of our data therefore, both a priori and a posteriori, led to thematic representation under six environments; individual, political, physical, technological, economic and socio-cultural environments. Within each environment we coded specific aspects such as ‘time to take part in activities’ (individual) or ‘allocation of funding’ (economic). We found that many codes overlapped several environments, as socio-ecological modelling encourages, to show multi-faceted influences on behaviour. The full list of codes and the environments these fit within can be found in supporting information. Our list of levels of influence and example codes can be found in Table 1.’

We have now provided detailed explanation of size, position and nesting/overlap in the caption below the figure of the model.

We have also strengthened our discussion of the model in the discussion section;

‘Our findings state the need for socio-cultural influences to be considered develop tangible, multi-faceted and contextually-appropriate ways to engage communities in health programmes. Our approach sits in contrast to individualised approaches to community health such as monetary incentives which only consider the individual(15,16) rather than the broader, wider, multi-faceted influences on individual behaviour.’

We further elaborate on how our SEM model can be used in our discussion section:

‘The strength of SEM’s is that they can be used to understand a range of factors which influence people’s behaviour. SEMs can be used to develop strategies which can span multiple environments such as identifying community members who lack technological skills and equipment to engage in certain community engagement activities, and then developing training, providing equipment and ensuring social support is given to develop individual’s technical skills and support them in their engagement in health programmes such as BSB. Such strategies cross technological, economic, socio-cultural and individual environments in the model to represent a complex intervention which considers multiple influences on behaviour.

In our study, we have developed a SEM which allows us to develop a more nuanced understanding of the reasons why communities may or may not engage with health programmes such as BSB. Using this knowledge we can begin to develop interventions and strategies which consider all the influences on the individual illustrated in our model to tackle lack of engagement in health programmes. Such strategies go beyond the individualised methods of incentivising individual behaviour and see community engagement in health programmes as multi-faceted and multi-factorial.’

7. On a related note, table 1 is unuseful to the reader. It is not easily readable, and is largely redundant with material in the main text. Perhaps a more synthetic table with key take-home points could be made, and the table contents as they exist now be moved to supplemental information. Thank you, we agree that Table 1 was long and detailed. We have moved this table to supplemental information. We have now created a new table 1 which illustrates in a much more succinct and clear manner the levels of influence developed in our analysis with examples from the original table.

8. The manuscript is riddled with small errors of grammar and punctuation. Thank you for this observation, we apologise for the errors and we have re-read the manuscript carefully to correct them.

9. Please indicate the mechanism by which your data will be made available. Thank you for this observation. Our study is a qualitative study. PLOS One states on in your instructions for qualitative data the following:

‘For studies analyzing data collected as part of qualitative research, authors should make excerpts of the transcripts relevant to the study available in an appropriate data repository, within the paper, or upon request if they cannot be shared publicly. If even sharing excerpts would violate the agreement to which the participants consented, authors should explain this restriction and what data they are able to share in their Data Availability Statement.’

The process from which data in this article is derived was an exercise in service design which would go on to inform future service evaluation of community engagement in the Better Start Bradford programme. As such it did not require formal ethical review approval (HRA decision 60/88/81). All participants in the workshops provided written consent for their data to be used anonymously and for findings derived from the service design process to be published in this article. For this reason, no identifying personal information was obtained or recorded for the purposes of research or any other use. The data which the research team holds is in the form of informal notes from the workshops which took place. This data is not in the form of formal transcripts but these researcher notes can be made available upon request. Detailed data gathered from the study showing the key themes discussed can be found in supporting information 2. This forms our minimal data set. Our Data Availability Statement will be updated accordingly.

Reviewer 1 comments Response to Reviewer 1 comments

Finally, the author suggests that considering multiple environmental influences and incorporation of the socio-cultural environment can be more effectively developed the community engagement activities. Thank you for these positive comments and observations, we really appreciate them.

Due to the specificity of these data, further detailed explanations of the ethnic, economic, and cultural backgrounds of the restricted areas or participated communities are needed to avoid the error of hasty generalization and to specify the scope of application of the model. Thank you for these observations. As explained above we have expanded our limitations section and substantially strengthened our section on the detail of the three wards which our study covers in Bradford. (see Setting section)

In our limitations section we state that:

‘We also acknowledge that there are limitations with relying on the data collected from the service design process. Our synthesis of the key themes which formed the environments in the socio-ecological model was based on data which was not explicitly designed to inform such a model. The questions asked and discussions had during the workshops were focused on the formation of a logic model for community engagement with BSB and not specifically on the formation of our socio-ecological model. Ideally we would conduct more workshops specifically aimed at further developing, fleshing out and strengthening the socio-ecological model we have proposed.’

The framework analysis and developed matrix structure of key socio-ecological themes are adopted to analyze the data. Although framework analysis lists environments that influence community engagement, the evidence in the results is insufficiently descriptive so statistical analysis or regression methods are recommended to support the results. We appreciate your suggestions. However as this is a qualitative study, we don’t believe our sample size would be large enough for statistical analysis. Furthermore the aim of the paper is to go in depth not search for representativeness or generalisability as statistical analysis or regression methods would do. As this well cited paper states (Flyvbjerg, 2006), case study research such as ours has merit and can strengthen our understanding of our social environment: https://journals.sagepub.com/doi/abs/10.1177/1077800405284363

Reviewer 2 comments Author response to reviewer 2 comments

This paper provides a general sociological look at the complexity of community engagement related to a health program, offering insights into the interconnected and multifaceted aspects of community life that might limit or enable engagement among diverse individuals. The use of community focus groups and workshops seems appropriate to the research questions. I have some concerns with the framing, novelty, contribution, and overall clarity of the article. Thank you, we believe we have fully addressed these issues now. We have changed the description of our methodology to that of workshops. We have also reframed our research question, explained in depth how our approach is novel and that it contributes to an as yet unexplored area of research. We have thoroughly rewritten large sections of the article and ensured a clear message runs throughout it.

Overall, I found the framing of the methods to be confusing. Either the authors are doing a sociological evaluation of a “coproduced” program, or they are trying to co-produce research findings within a sociological model (but not really using any coproduction methods), but the objectives are unclear and mismatched with the methods and findings. Please see our response to Editorial comment 4. Apologies for the confusion, made have made our methods clearer and coproduction has been largely removed from the article.

The introduction needs to provide a broader view of the need for this sort of work in terms of theoretical or methodological developments, criticism, evaluation, or novel contributions. Please see response to editorial comment 1. We have now thoroughly rewritten the introduction. The introduction now places this research in the context of the broader research question – increasing community involvement in health programmes. We explain how this paper tries to move beyond considering individual incentives to look more broadly at wider environments and factors which can influence participation in community health programmes.

You are clearly applying a co-produced research method for good reason, but why this would be useful or informative to the journal’s audience is unconvincing. There is really limited discussion or nuance around the theory of co-production here, or health related co-production work. Further, the language you use to describe the community is so vague and at patronizing. Did the participants in this coproduced project think of themselves as “ethnically diverse and deprived”? Which ethnicities? Do sociologists use the term deprived? If so, do you have a citation? Or some quantitative data to inform us of the demographics and trends in this area relative to your terms? Please see response to editorial comment 4 regarding our use of co-production. We have removed the terms ethnically diverse and deprived. We have provided detailed statistics about the ethnicities in the research area. See also our response to editorial comment 3 above.

It is unclear what sort of entity is running the project, relative to the community leadership and the various layers of governance and organizing that could be going on in the area. Is this a university? A local or national government? What role did the community have in security the funding, or setting project goals? We have clarified that the SEM (socio-ecological model) wasn’t coproduced only the logic model was coproduced. See editorial comment 4 above.

We have clarified that the research is situated within the BSB (Better Start Bradford) programme and the researchers running the project are from the BSBIH (Better Start Bradford Innovation Hub) which is the evaluation arm of the BSB programme located in BRI (Bradford Royal Infirmary) and BIHR (Bradford Institute for Health Research).

We have also clarified where funding for BSB came from in the Funding Declaration.

I would suggest finding an alternative to the term “stakeholder.” More specific terms have been used directly after the term stakeholder is used (once in the article) to qualify what we mean by the word.

I would suggest using direct language and taking up an active voice to more clearly indicate various actions and sources of agency and activity in your narrative. Thank you, we have gone through the article and ensured that we are using a more active voice to denote agency.

How interesting that program design is not considered research and does not require ethics clearance. I understand this is a strange “no-researcher-land” where co-produced research is concerned, however I’m also concerned that you have not cited the large body of ethics research, particularly ethics for working with underserved communities of color in research, in your ethics section. You have not outlined efforts to provide information or reciprocity back to your community participants in any form. If the Scottish standards for workshops of this type provide some details here, those should be explicitly documented in the methods. As detailed in response to editorial comment 4 above, we have clarified that we are not using co-production to create our SEM. Rather we have taken data from what was a co-production process to create a logic model as part of a service design process. We have taken advice from HRA and they confirm that the data generated from the service design process did not require formal ethical review approval (HRA decision 60/88/81). Therefore the ethics relating to the data generated from the logic model process were sound. We did not use any identifying data in our process and all participants in the original service design proves provided written consent for their data to be used and findings published.

Your participant recruitment methods are very unlearn. Thank you for this observation, we have clarified our recruitment methods, please see our response to editorial comment 2 above.

Did you perhaps also refer to the literature while conducting this analysis? If so, which theoretical body of work informed your assessment of the data? Do you have any citations to suggest evidence of how you enhanced the trustworthiness and validity, or relationship to theoretical grounding, in your work? Thank you we have corrected our method from co-production to workshop research. We have provided a detailed description of how we used literature to formulate our analysis process and coding framework now at the start of our results section. We believe the citations of the literature we provide here improve the trustworthiness and validity of our study and provide us with further theoretical grounding.

Decision Letter 1

11 Sep 2022

PONE-D-21-26119R1

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Ghaffar Ali, PhD

Additional Editor Comments (optional):

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

2. Is the manuscript technically sound, and do the data support the conclusions?

3. Has the statistical analysis been performed appropriately and rigorously?

4. Have the authors made all data underlying the findings in their manuscript fully available?

5. Is the manuscript presented in an intelligible fashion and written in standard English?

6. Review Comments to the Author

Reviewer #1: As the revised manuscript satisfies the publication criteria, I approve of its publication.

The detailed explanation of the ethnic, economic, and cultural backgrounds of the study areas and participant communities are included in setting and the limitation section. Also, terminology modified to accepted and appropriate terminology for the study community.

In addition, I realized that a case study was used appropriately rather than a statistical analysis for the qualitative study. The literature reviews of the mentioned studies, sociological, and pertinent applied disciplines were beneficial for approval.

7. PLOS authors have the option to publish the peer review history of their article ( what does this mean? ). If published, this will include your full peer review and any attached files.

Acceptance letter

13 Sep 2022

Dear Dr. Caperon:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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Prof. Ghaffar Ali