research paper on error analysis

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Second language acquisition: error analysis.

Published online by Cambridge University Press:  19 November 2008

The collection, classification, and analysis of errors in the written and spoken performance of second or foreign language learners has had a role in language pedagogy since at least the 1950s. However, in the late 60s, and paticularly in the 70s, the study of errors in non-native language performance, or Errors Analysis (EA), assumed a new role in applied linguistics. A more rigorous methodology for EA developed, and it was applied to new issues and questions within second language acquisition research. Yet, by 1980, EA was largely considered a transitional development in applied linguistics. This review considers the nature, development, and achievements of Error Analysis in the period from 1970 to 1980. We will consider EA from three perspectives, reviewing the use of Error Analysis: (1) to account for linguistic competence; (2) to identify learning processes and strategies, and (3) to provied input to language pedagogy.

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• Jack C. Richards
• DOI: https://doi.org/10.1017/S0267190500000520

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• Published: 30 January 2018

Teaching and learning mathematics through error analysis

• Sheryl J. Rushton 1  

Fields Mathematics Education Journal volume  3 , Article number:  4 ( 2018 ) Cite this article

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For decades, mathematics education pedagogy has relied most heavily on teachers, demonstrating correctly worked example exercises as models for students to follow while practicing their own exercises. In more recent years, incorrect exercises have been introduced for the purpose of student-conducted error analysis. Combining the use of correctly worked exercises with error analysis has led researchers to posit increased mathematical understanding. Combining the use of correctly worked exercises with error analysis has led researchers to posit increased mathematical understanding.

A mixed method design was used to investigate the use of error analysis in a seventh-grade mathematics unit on equations and inequalities. Quantitative data were used to establish statistical significance of the effectiveness of using error analysis and qualitative methods were used to understand participants’ experience with error analysis.

The results determined that there was no significant difference in posttest scores. However, there was a significant difference in delayed posttest scores.

In general, the teacher and students found the use of error analysis to be beneficial in the learning process.

For decades, mathematics education pedagogy has relied most heavily on teachers demonstrating correctly worked example exercises as models for students to follow while practicing their own exercises [ 3 ]. In more recent years, incorrect exercises have been introduced for the purpose of student-conducted error analysis [ 17 ]. Conducting error analysis aligns with the Standards of Mathematical Practice [ 18 , 19 ] and the Mathematics Teaching Practices [ 18 ]. Researchers posit a result of increased mathematical understanding when these practices are used with a combination of correctly and erroneously worked exercises [ 1 , 4 , 8 , 11 , 15 , 16 , 18 , 19 , 23 ].

Review of literature

Correctly worked examples consist of a problem statement with the steps taken to reach a solution along with the final result and are an effective method for the initial acquisitions of procedural skills and knowledge [ 1 , 11 , 26 ]. Cognitive load theory [ 1 , 11 , 25 ] explains the challenge of stimulating the cognitive process without overloading the student with too much essential and extraneous information that will limit the working memory and leave a restricted capacity for learning. Correctly worked examples focus the student’s attention on the correct solution procedure which helps to avoid the need to search their prior knowledge for solution methods. Correctly worked examples free the students from performance demands and allow them to concentrate on gaining new knowledge [ 1 , 11 , 16 ].

Error analysis is an instructional strategy that holds promise of helping students to retain their learning [ 16 ]. Error analysis consists of being presented a problem statement with the steps taken to reach a solution in which one or more of the steps are incorrect, often called erroneous examples [ 17 ]. Students analyze and explain the errors and then complete the exercise correctly providing reasoning for their own solution. Error analysis leads students to enact two Standards of Mathematical Practice, namely, (a) make sense of problems and persevere in solving them and (b) attend to precision [ 19 ].

Another of the Standards of Mathematical Practice suggests that students learn to construct viable arguments and comment on the reasoning of others [ 19 ]. According to Große and Renkl [ 11 ], students who attempted to establish a rationale for the steps of the solution learned more than those who did not search for an explanation. Teachers can assist in this practice by facilitating meaningful mathematical discourse [ 18 ]. “Arguments do not have to be lengthy, they simply need to be clear, specific, and contain data or reasoning to back up the thinking” [ 20 ]. Those data and reasons could be in the form of charts, diagrams, tables, drawings, examples, or word explanations.

Researchers [ 7 , 21 ] found the process of explaining and justifying solutions for both correct and erroneous examples to be more beneficial for achieving learning outcomes than explaining and justifying solutions to correctly worked examples only. They also found that explaining why an exercise is correct or incorrect fostered transfer and led to better learning outcomes than explaining correct solutions only. According to Silver et al. [ 22 ], students are able to form understanding by classifying procedures into categories of correct examples and erroneous examples. The students then test their initial categories against further correct and erroneous examples to finally generate a set of attributes that defines the concept. Exposing students to both correctly worked examples and error analysis is especially beneficial when a mathematical concept is often done incorrectly or is easily confused [ 11 ].

Große and Renkl [ 11 ] suggested in their study involving university students in Germany that since errors are inherent in human life, introducing errors in the learning process encourages students to reflect on what they know and then be able to create clearer and more complete explanations of the solutions. The presentation of “incorrect knowledge can induce cognitive conflicts which prompt the learner to build up a coherent knowledge structure” [ 11 ]. Presenting a cognitive conflict through erroneously worked exercises triggers learning episodes through reflection and explanations, which leads to deeper understanding [ 29 ]. Error analysis “can foster a deeper and more complete understanding of mathematical content, as well as of the nature of mathematics itself” [ 4 ].

Several studies have been conducted on the use of error analysis in mathematical units [ 1 , 16 , 17 ]. The study conducted for this article differed from these previous studies in mathematical content, number of teachers and students involved in the study, and their use of a computer or online component. The most impactful differences between the error analysis studies conducted in the past and this article’s study are the length of time between the posttest and the delayed posttest and the use of qualitative data to add depth to the findings. The previous studies found students who conducted error analysis work did not perform significantly different on the posttest than students who received a more traditional approach to learning mathematics. However, the students who conducted error analysis outperformed the control group in each of the studies on delayed posttests that were given 1–2 weeks after the initial posttest.

Loibl and Rummel [ 15 ] discovered that high school students became aware of their knowledge gaps in a general manner by attempting an exercise and failing. Instruction comparing the erroneous work with correctly worked exercises filled the learning gaps. Gadgil et al. [ 9 ] conducted a study in which students who compared flawed work to expertly done work were more likely to repair their own errors than students who only explained the expertly done work. This discovery was further supported by other researchers [8, 14, 24]. Each of these researchers found students ranging from elementary mathematics to university undergraduate medical school who, when given correctly worked examples and erroneous examples, learned more than students who only examined correctly worked examples. This was especially true when the erroneous examples were similar to the kinds of errors that they had committed [ 14 ]. Stark et al. [ 24 ] added that it is important for students to receive sufficient scaffolding in correctly worked examples before and alongside of the erroneous examples.

The purpose of this study was to explore whether seventh-grade mathematics students could learn better from the use of both correctly worked examples and error analysis than from the more traditional instructional approach of solving their exercises in which the students are instructed with only correctly worked examples. The study furthered previous research on the subject of learning from the use of both correctly worked examples and error analysis by also investigating the feedback from the teacher’s and students’ experiences with error analysis. The following questions were answered in this study:

What was the difference in mathematical achievement when error analysis was included in students’ lessons and assignments versus a traditional approach of learning through correct examples only?

What kind of benefits or disadvantages did the students and teacher observe when error analysis was included in students’ lessons and assignments versus a traditional approach of learning through correct examples only?

A mixed method design was used to investigate the use of error analysis in a seventh-grade mathematics unit on equations and inequalities. Quantitative data were used to establish statistical significance of the effectiveness of using error analysis and qualitative methods were used to understand participants’ experience with error analysis [ 6 , 27 ].

Participants

Two-seventh-grade mathematics classes at an International Baccalaureate (IB) school in a suburban charter school in Northern Utah made up the control and treatment groups using a convenience grouping. One class of 26 students was the control group and one class of 27 students was the treatment group.

The same teacher taught both the groups, so a comparison could be made from the teacher’s point of view of how the students learned and participated in the two different groups. At the beginning of the study, the teacher was willing to give error analysis a try in her classroom; however, she was not enthusiastic about using this strategy. She could not visualize how error analysis could work on a daily basis. By the end of the study, the teacher became very enthusiastic about using error analysis in her seventh grade mathematics classes.

The total group of participants involved 29 males and 24 females. About 92% of the participants were Caucasian and the other 8% were of varying ethnicities. Seventeen percent of the student body was on free or reduced lunch. Approximately 10% of the students had individual education plans (IEP).

A pretest and posttest were created to contain questions that would test for mathematical understanding on equations and inequalities using Glencoe Math: Your Common Core Edition CCSS [ 5 ] as a resource. The pretest was reused as the delayed posttest. Homework assignments were created for both the control group and the treatment group from the Glencoe Math: Your Common Core Edition CCSS textbook. However, the researcher rewrote two to three of the homework exercises as erroneous examples for the treatment group to find the error and fix the exercise with justifications (see Figs.  1 , 2 ). Students from both groups used an Assignment Time Log to track the amount of time which they spent on their homework assignments.

Example of the rewritten homework exercises as equation erroneous examples

Example of the rewritten homework exercises as inequality erroneous examples

Both the control and the treatment groups were given the same pretest for an equations and inequality unit. The teacher taught both the control and treatment groups the information for the new concepts in the same manner. The majority of the instruction was done using the direct instruction strategy. The students in both groups were allowed to work collaboratively in pairs or small groups to complete the assignments after instruction had been given. During the time she allotted for answering questions from the previous assignment, she would only show the control group the exercises worked correctly. However, for the treatment group, the teacher would write errors which she found in the students’ work on the board. She would then either pair up the students or create small groups and have the student discuss what errors they noticed and how they would fix them. Often, the teacher brought the class together as a whole to discuss what they discovered and how they could learn from it.

The treatment group was given a homework assignment with the same exercises as the control group, but including the erroneous examples. Students in both the control and treatment groups were given the Assignment Time Log to keep a record of how much time was spent completing each homework assignment.

At the end of each week, both groups took the same quiz. The quizzes for the control group received a grade, and the quiz was returned without any further attention. If a student asked how to do an exercise, the teacher only showed the correct example. The teacher graded the quizzes for the treatment group using the strategy found in the Teaching Channel’s video “Highlighting Mistakes: A Grading Strategy” [ 2 ]. She marked the quizzes by highlighting the mistakes; no score was given. The students were allowed time in class or at home to make corrections with justifications.

The same posttest was administered to both groups at the conclusion of the equation and inequality chapter, and a delayed posttest was administered 6 weeks later. The delayed posttest also asked the students in the treatment group to respond to an open-ended request to “Please provide some feedback on your experience”. The test scores were analyzed for significant differences using independent samples t tests. The responses to the open-ended request were coded and analyzed for similarities and differences, and then, used to determine the students’ perceptions of the benefits or disadvantages of using error analysis in their learning.

At the conclusion of gathering data from the assessments, the researcher interviewed the teacher to determine the differences which the teacher observed in the preparation of the lessons and students’ participation in the lessons [ 6 ]. The interview with the teacher contained a variety of open-ended questions. These are the questions asked during the interview: (a) what is your opinion of using error analysis in your classroom at the conclusion of the study versus before the study began? (b) describe a typical classroom discussion in both the control group class and the treatment group class, (c) talk about the amount of time you spent grading, preparing, and teaching both groups, and (d) describe the benefits or disadvantages of using error analysis on a daily basis compared to not using error analysis in the classroom. The responses from the teacher were entered into a computer, coded, and analyzed for thematic content [ 6 , 27 ]. The themes that emerged from coding the teacher’s responses were used to determine the kind of benefits or disadvantages observed when error analysis was included in students’ lessons and assignments versus a traditional approach of learning through correct examples only from the teacher’s point of view.

Findings and discussion

Mathematical achievement.

Preliminary analyses were carried out to evaluate assumptions for the t test. Those assumptions include: (a) the independence, (b) normality tested using the Shapiro–Wilk test, and (c) homogeneity of variance tested using the Levene Statistic. All assumptions were met.

The Levene Statistic for the pretest scores ( p  > 0.05) indicated that there was not a significant difference in the groups. Independent samples t tests were conducted to determine the effect error analysis had on student achievement determined by the difference in the means of the pretest and posttest and of the pretest and delayed posttest. There was no significant difference in the scores from the posttest for the control group ( M  = 8.23, SD = 5.67) and the treatment group ( M  = 9.56, SD = 5.24); t (51) = 0.88, p  = 0.381. However, there was a significant difference in the scores from the delayed posttest for the control group ( M  = 5.96, SD = 4.90) and the treatment group ( M  = 9.41, SD = 4.77); t (51) = 2.60, p  = 0.012. These results suggest that students can initially learn mathematical concepts through a variety of methods. Nevertheless, the retention of the mathematical knowledge is significantly increased when error analysis is added to the students’ lessons, assignments, and quizzes. It is interesting to note that the difference between the means from the pretest to the posttest was higher in the treatment group ( M  = 9.56) versus the control group ( M  = 8.23), implying that even though there was not a significant difference in the means, the treatment group did show a greater improvement.

The Assignment Time Log was completed by only 19% of the students in the treatment group and 38% of the students in the control group. By having such a small percentage of each group participate in tracking the time spent completing homework assignment, the results from the t test analysis cannot be used in any generalization. However, the results from the analysis were interesting. The mean time spent doing the assignments for each group was calculated and analyzed using an independent samples t test. There was no significant difference in the amount of time students which spent on their homework for the control group ( M  = 168.30, SD = 77.41) and the treatment group ( M  = 165.80, SD = 26.53); t (13) = 0.07, p  = 0.946. These results suggest that the amount of time that students spent on their homework was close to the same whether they had to do error analyses (find the errors, fix them, and justify the steps taken) or solve each exercise in a traditional manner of following correctly worked examples. Although the students did not spend a significantly different amount of time outside of class doing homework, the treatment group did spend more time during class working on quiz corrections and discussing error which could attribute to the retention of knowledge.

Feedback from participants

All students participating in the current study submitted a signed informed consent form. Students process mathematical procedures better when they are aware of their own errors and knowledge gaps [ 15 ]. The theoretical model of using errors that students make themselves and errors that are likely due to the typical knowledge gaps can also be found in works by other researchers such as Kawasaki [ 14 ] and VanLehn [ 29 ]. Highlighting errors in the students’ own work and in typical errors made by others allowed the participants in the treatment group the opportunity to experience this theoretical model. From their experiences, the participants were able to give feedback to help the researcher delve deeper into what the thoughts were of the use of error analysis in their mathematics classes than any other study provided [ 1 , 4 , 7 , 8 , 9 , 11 , 14 , 15 , 16 , 17 , 21 , 23 , 24 , 25 , 26 , 29 ]. Overall, the teacher and students found the use of error analysis in the equations and inequalities unit to be beneficial. The teacher pointed out that the discussions in class were deeper in the treatment group’s class. When she tried to facilitate meaningful mathematical discourse [ 18 ] in the control group class, the students were unable to get to the same level of critical thinking as the treatment group discussions. In the open-ended question at the conclusion of the delayed posttest (“Please provide some feedback on your experience.”), the majority (86%) of the participants from the treatment group indicated that the use of erroneous examples integrated into their lessons was beneficial in helping them recognize their own mistakes and understanding how to correct those mistakes. One student reported, “I realized I was doing the same mistakes and now knew how to fix it”. Several (67%) of the students indicated learning through error analysis made the learning process easier for them. A student commented that “When I figure out the mistake then I understand the concept better, and how to do it, and how not to do it”.

When students find and correct the errors in exercises, while justifying themselves, they are being encouraged to learn to construct viable arguments and critique the reasoning of others [ 19 ]. This study found that explaining why an exercise is correct or incorrect fostered transfer and led to better learning outcomes than explaining correct solutions only. However, some of the higher level students struggled with the explanation component. According to the teacher, many of these higher level students who typically do very well on the homework and quizzes scored lower on the unit quizzes and tests than the students expected due to the requirement of explaining the work. In the past, these students had not been justifying their thinking and always got correct answers. Therefore, providing reasons for erroneous examples and justifying their own process were difficult for them.

Often teachers are resistant to the idea of using error analysis in their classroom. Some feel creating erroneous examples and highlighting errors for students to analyze is too time-consuming [ 28 ]. The teacher in this study taught both the control and treatment groups, which allowed her the perspective to compare both methods. She stated, “Grading took about the same amount of time whether I gave a score or just highlighted the mistakes”. She noticed that having the students work on their errors from the quizzes and having them find the errors in the assignments and on the board during class time ultimately meant less work for her and more work for the students.

Another reason behind the reluctance to use error analysis is the fact that teachers are uncertain about exposing errors to their students. They are fearful that the discussion of errors could lead their students to make those same errors and obtain incorrect solutions [ 28 ]. Yet, most of the students’ feedback stated the discussions in class and the error analyses on the assignments and quizzes helped them in working homework exercises correctly. Specifically, they said figuring out what went wrong in the exercise helped them solve that and other exercises. One student said that error analysis helped them “do better in math on the test, and I actually enjoyed it”. Nevertheless, 2 of the 27 participating students in the treatment group had negative comments about learning through error analysis. One student did not feel that correcting mistakes showed them anything, and it did not reinforce the lesson. The other student stated being exposed to error analysis did, indeed, confuse them. The student kept thinking the erroneous example was a correct answer and was unsure about what they were supposed to do to solve the exercise.

When the researcher asked the teacher if there were any benefits or disadvantages to using error analysis in teaching the equations and inequalities unit, she said that she thoroughly enjoyed teaching using the error analysis method and was planning to implement it in all of her classes in the future. In fact, she found that her “hands were tied” while grading the control group quizzes and facilitating the lessons. She said, “I wanted to have the students find their errors and fix them, so we could have a discussion about what they were doing wrong”. The students also found error analysis to have more benefits than disadvantages. Other than one student whose response was eliminated for not being on topic and the two students with negative comments, the other 24 of the students in the treatment group had positive comments about their experience with error analysis. When students had the opportunity to analyze errors in worked exercises (error analysis) through the assignments and quizzes, they were able to get a deeper understanding of the content and, therefore, retained the information longer than those who only learned through correct examples.

Discussions generated in the treatment group’s classroom afforded the students the opportunity to critically reason through the work of others and to develop possible arguments on what had been done in the erroneous exercise and what approaches might be taken to successfully find a solution to the exercise. It may seem surprising that an error as simple as adding a number when it should have been subtracted could prompt a variety of questions and lead to the students suggesting possible ways to solve and check to see if the solution makes sense. In an erroneous exercise presented to the treatment group, the students were provided with the information that two of the three angles of a triangle were 35° and 45°. The task was to write and solve an equation to find the missing measure. The erroneous exercise solver had created the equation: x  + 35 + 45 = 180. Next was written x  + 80 = 180. The solution was x  = 260°. In the discussion, the class had on this exercise, the conclusion was made that the error occurred when 80 was added to 180 to get a sum of 260. However, the discussion progressed finding different equations and steps that could have been taken to discover the missing angle measure to be 100° and why 260° was an unreasonable solution. Another approach discussed by the students was to recognize that to say the missing angle measure was 260° contradicted with the fact that one angle could not be larger than the sum of the angle measures of a triangle. Analyzing the erroneous exercises gave the students the opportunity of engaging in the activity of “explaining” and “fixing” the errors of the presented exercise as well as their own errors, an activity that fostered the students’ learning.

The students participating in both the control and treatment groups from the two-seventh-grade mathematics classes at the IB school in a suburban charter school in Northern Utah initially learned the concepts taught in the equations and inequality unit statistically just as well with both methods of teaching. The control group had the information taught to them with the use of only correctly worked examples. If they had a question about an exercise which they did wrong, the teacher would show them how to do the exercise correctly and have a discussion on the steps required to obtain the correct solutions. On their assignments and quizzes, the control group was expected to complete the work by correctly solving the equations and inequalities in the exercise, get a score on their work, and move on to the next concept. On the other hand, the students participating in the treatment group were given erroneous examples within their assignments and asked to find the errors, explain what had been done wrong, and then correctly solve the exercise with justifications for the steps they chose to use. During lessons, the teacher put erroneous examples from the students’ work on the board and generated paired, small groups, or whole group discussion of what was wrong with the exercise and the different ways to do it correctly. On the quizzes, the teacher highlighted the errors and allowed the students to explain the errors and justify the correct solution.

Both the method of teaching using error analysis and the traditional method of presenting the exercise and having the students solve it proved to be just as successful on the immediate unit summative posttest. However, the delayed posttest given 6 weeks after the posttest showed that the retention of knowledge was significantly higher for the treatment group. It is important to note that the fact that the students in the treatment group were given more time to discuss the exercises in small groups and as a whole class could have influenced the retention of mathematical knowledge just as much or more than the treatment of using error analysis. Researchers have proven academic advantages of group work for students, in large part due to the perception of students having a secure support system, which cannot be obtained when working individually [ 10 , 12 , 13 ].

The findings of this study supported the statistical findings of other researchers [ 1 , 16 , 17 ], suggesting that error analysis may aid in providing a richer learning experience that leads to a deeper understanding of equations and inequalities for long-term knowledge. The findings of this study also investigated the teacher’s and students’ perceptions of using error analysis in their teaching and learning. The students and teacher used for this study were chosen to have the same teacher for both the control and treatment groups. Using the same teacher for both groups, the researcher was able to determine the teacher’s attitude toward the use of error analysis compared to the non-use of error analysis in her instruction. The teacher’s comments during the interview implied that she no longer had an unenthusiastic and skeptical attitude toward the use of error analysis on a daily basis in her classroom. She was “excited to implement the error analysis strategy into the rest of her classes for the rest of the school year”. She observed error analysis to be an effective way to deal with common misconceptions and offer opportunities for students to reflect on their learning from their errors. The process of error analysis assisted the teacher in supporting productive struggle in learning mathematics [ 18 ] and created opportunity for students to have deep discussions about alternative ways to solve exercises. Error analysis also aided in students’ discovery of their own errors and gave them possible ways to correct those errors. Learning through the use of error analysis was enjoyable for many of the participating students.

According to the NCTM [ 18 ], effective teaching of mathematics happens when a teacher implements exercises that will engage students in solving and discussing tasks that promote mathematical reasoning and problem solving. Providing erroneous examples allowed discussion, multiple entry points, and varied solution strategies. Both the teacher and the students participating in the treatment group came to the conclusion that error analysis is a beneficial strategy to use in the teaching and learning of mathematics. Regardless of the two negative student comments about error analysis not being helpful for them, this researcher recommends the use of error analysis in teaching and learning mathematics.

The implications of the treatment of teaching students mathematics through the use of error analysis are that students’ learning could be fostered and retention of content knowledge may be longer. When a teacher is able to have their students’ practice critiquing the reasoning of others and creating viable arguments [ 19 ] by analyzing errors in mathematics, the students not only are able to meet the Standard of Mathematical Practice, but are also creating a lifelong skill of analyzing the effectiveness of “plausible arguments, distinguish correct logic or reasoning from that which is flawed, and—if there is a flaw in an argument—explain what it is” ([ 19 ], p. 7).

Limitations and future research

This study had limitations. The sample size was small to use the same teacher for both groups. Another limitation was the length of the study only encompassed one unit. Using error analysis could have been a novelty and engaged the students more than it would when the novelty wore off. Still another limitation was the study that was conducted at an International Baccalaureate (IB) school in a suburban charter school in Northern Utah, which may limit the generalization of the findings and implications to other schools with different demographics.

This study did not have a separation of conceptual and procedural questions on the assessments. For a future study, the creation of an assessment that would be able to determine if error analysis was more helpful in teaching conceptual mathematics or procedural mathematics could be beneficial to teachers as they plan their lessons. Another suggestion for future research would be to gather more data using several teachers teaching both the treatment group and the control group.

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Rushton, S.J. Teaching and learning mathematics through error analysis. Fields Math Educ J 3 , 4 (2018). https://doi.org/10.1186/s40928-018-0009-y

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Error analysis of measurement uncertainty: a snapshot literature review in field of medicine and health in China

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• Published: 25 July 2023
• Volume 28 , pages 245–249, ( 2023 )

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• Manqing Nie 1 ,
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To analyze and statistically compare common errors in the evaluation of measurement uncertainty in medicine and health field, using literature research and comparison with national standards, in order to understand the current status of measurement uncertainty evaluation in the medicine and health field. Using Chinese National Knowledge Infrastructure (CNKI) as the sample population, Stratified Proportional Sampling (PPS) was used to extract journal articles related to measurement uncertainty in the field of medicine and health. The articles were compared with the Eurachem/CITAC Guide QUAM to analyze measurement uncertainty errors. Academic attention to measurement uncertainty in the field of medicine and health in the CNKI literature database has shown explosive growth since 2005. Seven common errors in measurement uncertainty evaluation were identified. None of the 30 journal articles analyzed were error-free, with a total error rate of 44 %. The error rate for ignorance of blank uncertainty was 87 %, improper evaluation of standard curve was 67 %, improper significant figures were 60 %, and insufficient information for Type B evaluation was 50 %. The error rate for provincial and higher-level institutions was 48 %, while the error rate for institutions below the provincial level was 43 %. The difference between the two error rates was not statistically significant ( p  = 0.523). There is an urgent need to improve the rationality of measurement uncertainty evaluation in medicine and health field, and to strengthen the education and academic communication through national and international cooperation.

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Introduction

With the improvement of laboratory management practices, increased state accreditation, and the growing popularity of national laboratory accreditation in China's medicine and health field, the process of uncertainty evaluation in medicine and health laboratories has gained more attention and recognition. The promotion of uncertainty evaluation in China has almost reached its 20th anniversary, from the guidance of GUM:1995 [ 1 ] to JJF 1059–1999 [ 2 ], JJF 1059.1–2012 [ 3 ], and the implementation of QUAM 3rd (CNAS GL06) [ 4 , 5 ] and RB/T151-2016 [ 6 ] for uncertainty evaluation guidelines in chemical measurement and microbial quantitative analysis. The development of uncertainty evaluation in medicine and health is not as rapid and widespread as in the fields of physics and calibration laboratories, but in recent years, it has rapidly progressed and has become a routine work required for laboratory accreditation such as CNAS and CMA. However, understanding the rationality of uncertainty evaluation has always been challenging, leading to numerous errors in daily evaluations. This makes it difficult for frontline personnel to objectively assess uncertainty and obtain reliable and accurate results. In addition, there is an urgent lack of literature on uncertainty error analysis for reference among peers. Therefore, this paper aims to analyze the common errors in uncertainty evaluation in the field of medicine and health and propose correct methods for evaluating measurement uncertainty, using literature research and international standard comparison.

Literature research and measurement methods

The China National Knowledge Infrastructure (CNKI) literature database was used for uncertainty literature research and measurement, using "uncertainty" as the keyword and “medicine and health” as the searching condition. The research period was not limited, and the temporal sequence of academic attention was analyzed using the CNKI literature index. As there were relatively few uncertainty literature before 2000, this research divided the journal literature into five periods: before 2000, 2001–2005, 2006–2010, 2011–2015, and 2017 to present, using the number of journal literature during these periods as the stratified sampling basis.

Literature sampling scheme

A total of 30 journal articles were selected as representatives for uncertainty error analysis and statistics, using a stratified proportional sampling method to sample the CNKI uncertainty assessment journal literature in the field of medicine and health.

Classification and basis of uncertainty errors

By comparing QUAM 3rd and CNAS GL06 (the Chinese translation of QUAM 3rd), seven types of errors were identified which included: E1. Incomplete mathematical models; E2. Errors in the standard curve formula evaluation; E3. Inappropriate standard series evaluation; E4. Insufficient information for type B evaluation; E5. Uncertainty of the blank not evaluated; E6. Improper recovery rate evaluation; E7. Improper significant figures. The qualitative description and explanation of these errors are shown in Table 1 .

Statistical methods

Rate comparison was performed using the chi-square test, which was conducted using IBM SPSS Statistics 19.

Results and discussion

Uncertainty academic attention and journal literature statistics.

The keyword uncertainty was selected from the CNKI literature database, with no time limit, and a total of 28,911 documents were retrieved. The literature on uncertainty in China has grown significantly since 2000 and peaked in 2013, then gradually declined. Foreign literature also shows a similar trend, but after 2013, there was a sharp increase in foreign literature, which may be related to the increasing attention to uncertainty in QUAM 3rd and the medicine and health field. The number of journal articles retrieved with “medicine and health” as the search restriction was 2568. The statistics of the publication quantity of Chinese journal articles are shown in Fig.  1 , which increased significantly after 2005 and reached its peak in 2011–2015. This may be related to compulsive national accreditation and laboratory accreditation requirements.

Trends in the number of publications of Chinese uncertainty journals in the Chinese National Knowledge Infrastructure (CNKI) literature database

Uncertainty error statistical analysis

Using the stratified proportional sampling method, 30 journal articles were randomly selected from the 2568 journal articles in the CNKI literature database, and the uncertainty error classification and statistics were carried out. The number of samples for each of the five periods, including before 2000, 2001–2005, 2006–2010, 2011–2015, and 2017 to present, was 0, 2, 9, 15, and 4, respectively. The articles were from 15 provinces and municipalities, including food and drug, health systems, and universities at the national, provincial, and lower levels. Among them, 11 articles used atomic spectroscopy methods including ICP/MS, 12 articles used chromatography methods including chromatography-mass spectrometry, 5 articles used photometric methods, and 1 article each used titration and enzyme-linked immunosorbent assay methods. The error rate statistics are shown in Table 2 . None of the 30 journal articles were error-free, and the total error rate was as high as 44 %. The four types of errors with higher error rates include: blank uncertainty without evaluation error rate of 87 %, improper standard series evaluation error rate of 67 %, inappropriate significant figures error rate of 60 %, and insufficient information of type B evaluation error rate of 50 %. The error rate of provincial and above institutions was 48 %, while the error rate of institutions below the provincial level was 43 %. The difference in error rates between the two was not statistically significant with p  = 0.523. This partially reflected that provincial and above institutions have not played a good technical guidance role in the correctness and rationality of uncertainty evaluation methods.

The academic attention to uncertainty in the CNKI literature database showed explosive growth after 2005, and the number of journal articles retrieved with "medicine and health" as the search restriction showed a similar growth trend. However, the total error rate of the 30 randomly sampled journal articles, compared with the seven types of errors summarized in QUAM 3rd and CNAS GL06 recommended terms, was as high as 44 %, and no journal article was error-free, indicating that the correctness and rationality of uncertainty evaluation in the medicine and health field are not optimistic even terrible. The difference in error rates between provincial and above units and units below the provincial level was not statistically significant ( p  = 0.523), partially reflecting that provincial and above institutions have not played a good technical guidance role in the correctness and rationality of uncertainty evaluation methods. The lack of education on measurement uncertainty in the medicine and health field, the quality of continuing education, and the lack of academic communication of uncertainty evaluation shows the pain points of the current severe situation. Improving the quality of uncertainty education and communication both multi-dimensionally and multi-facetedly should become an urgent need in the medicine and health field.

The current study may be subject to at least three possible limitations. First, due to the small sample size problem, there may be sampling error from literature research. Second, the keyword “medicine and health” was limited by Chinese expression, the published articles only indirectly reflected the errors in daily practices, but our results provided valuable reference meanings for present severe situation of measurement uncertainty in medicine and health field. Finally, as the current study did not involve the uncertainty evaluation of the microbiological field, a status assessment of this field could not be performed.

Joint Committee on Guides for Metrology (2008) Guide to the expression of uncertainty in measurement. Measurement Uncertainty, JCGM 100

JJF1059-1999 (1999) Evaluation and Expression of Uncertainty in Measurement, The State Bureau of Quality and Technical Supervision of the People's Republic of China

JJF1059.1-2012 (2012) Evaluation and Expression of Uncertainty in Measurement, General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China

EURACHEM/CITAC Guide (2012) Quantifying Uncertainty in Analytical Measurement, Third Edition. Laboratory of the Government Chemist, London

CNAS-GL06:2006 (2012) Guidance on Evaluating the Uncertainty in Chemical Analysis, China National Accreditation Service for Conformity Assessment

RB/T 151–2016 (2016) Guidelines for the estimation of measurement uncertainty of food microbiological quantitative detection, Certification and Accreditation Administration of the People’s Republic of China

Treatment of an observed bias (2022) Eurachem, Laboratory of the Government Chemist, London, 23 October. https://www.eurachem.org/index.php/publications/leaflets/bias-trt-01

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Nie, M., Chen, J. & Zheng, B. Error analysis of measurement uncertainty: a snapshot literature review in field of medicine and health in China. Accred Qual Assur 28 , 245–249 (2023). https://doi.org/10.1007/s00769-023-01549-8

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Negative emotions experienced by healthcare staff following medication administration errors: a descriptive study using text-mining and content analysis of incident data

• Sanu Mahat 1 ,
• Anne Marie Rafferty 2 ,
• Katri Vehviläinen-Julkunen 3 , 4 &
• Marja Härkänen 1  

BMC Health Services Research volume  22 , Article number:  1474 ( 2022 ) Cite this article

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Medication errors regardless of the degree of patient harm can have a negative emotional impact on the healthcare staff involved. The potential for self-victimization of healthcare staff following medication errors can add to the moral distress of healthcare staff. The stigma associated with errors and their disclosure often haunts healthcare professionals, leading them to question their own professional competence. This paper investigates the negative emotions expressed by healthcare staff in their reported medication administration error incidents along with the immediate responses they received from their seniors and colleagues after the incident.

This is a retrospective study using a qualitative descriptive design and text mining. This study includes free-text descriptions of medication administration error incidents ( n  = 72,390) reported to National Reporting & Learning System in 2016 from England and Wales. Text-mining by SAS text miner and content analysis was used to analyse the data.

Analysis of data led to the extraction of 93 initial codes and two categories i.e., 1) negative emotions expressed by healthcare staff which included 4 sub-categories of feelings: (i) fear; (ii) disturbed; (iii) sadness; (iv) guilt and 2) Immediate response from seniors and colleagues which included 2 sub-categories: (i) Reassurance and support and (ii) Guidance on what to do after an error.

Negative emotions expressed by healthcare staff when reporting medication errors could be a catalyst for learning and system change. However, negative emotions when internalized as fear, guilt, or self-blame, could have a negative impact on the mental health of individuals concerned, reporting culture, and opportunities for learning from the error. Findings from this study, hence, call for future research to investigate the impact of negative emotions on healthcare staff well-being and identify ways to mitigate these in practice.

Peer Review reports

Medication Errors (MEs) are recognized by the World Health Organization as the leading cause of injury and avoidable harm in healthcare, costing approximately 42 billion dollars annually, which is nearly 1% of total global health expenditure [ 1 ]. The safety of patients is at the forefront of the healthcare system; however, healthcare staff can also be traumatized by the aftermath of MEs. Although the healthcare mantra is “first do no harm”, healthcare professionals involved in adverse events can feel guilt, shame, anger, fear, and anxiety [ 2 ]. They are often neglected with only a few coping strategies and support systems available to help them [ 3 ]. Negative consequences of an adverse event can reach far beyond the “first victim” i.e., the patient. Thus, affecting healthcare staff psychologically making them “second victims” [ 4 ]. The term “second victim” was first coined by Dr. Albert Wu to explain the emotions of a young resident who committed an error and had experienced ridicule, shame, and lack of support, from his peers [ 2 ]. Although this concept was first applied to physicians, other healthcare staff, including nurses, also experience similar emotions. Scott et al. [ 5 ] described the term second victim as “a healthcare provider involved in an unanticipated adverse patient event, medical error and/or a patient-related injury who has become victimized in the sense that the provider is traumatized by the event. Frequently, second victims feel personally responsible for the unexpected patient outcomes and experience as though they have failed their patient, feeling doubts about their clinical skills and knowledge base”[ 5 ].

The use of the term second victim has been criticized recently [ 6 , 7 ] arguing that it might act as a way in which healthcare providers can evade responsibility and accountability and it might be offensive to affected patients and families [ 6 ]. Laying accountability at the door of an individual, ignoring the wider organizational ramifications of accountability in terms of the conditions which trigger errors in the first place, can let the organization off the hook. Even though the use of the term “victim” may sound spurious and uncomfortable to many healthcare professionals, patients, and families, it is indubitably an advantage in reinforcing the seriousness and urgency of the problem among policymakers and healthcare managers [ 8 ]. Wu et al.[ 8 ] have suggested the importance of the use of the term second victim as it is notable and denotes urgency. These assumptions regarding the use of the term second victim are inherent in both positions. Therefore, our research is designed to take this debate one step further by analyzing the consequences of errors in terms of emotional response and lived experiences of healthcare staff.

Regardless of the degree of patient harm, the mere thought of potential patient injury caused by ME is sufficient to induce the feelings of fear, distress, anger, anxiety, guilt and remorse in healthcare staff [ 9 , 10 , 11 ]. Although evidence suggests multiple system-based causes of MEs, the error-maker still tends to blame themselves i.e., they should have functioned proficiently [ 11 ]. If the seriousness of these issues remains unaddressed, it can negatively affect healthcare workers’ personal and professional well-being causing depression, burnout, Post Traumatic Stress Disorder (PTSD), and even suicidal thoughts [ 4 , 12 , 13 ]. Error prevention has therefore been a focus of major attention for healthcare organizations for years but the impact of MEs on the healthcare professional involved has received less attention. A more nuanced and textured exploration of the impact of the problem upon healthcare workers is required if preventative strategies are to be effective [ 11 ].

Previous studies have shown that often MEs causing harm are reported whereas near misses are often under-reported [ 14 ]. This underestimates the number of healthcare staff going through negative experiences [ 15 ]. Fear of legal consequences, blame, losing patients’ trust, and punishment have been recognized as barriers to ME reporting[ 16 ] leading healthcare staff to suffer in silence, sometimes struggling alone in isolation and burdened with a sense of shame [ 9 ]. Therefore, a system is needed to mitigate these barriers and create a “just culture guide” which helps healthcare managers to treat staff involved in adverse events fairly, support open and fair culture and maximize learning from errors [ 17 ]. However, it is apparent that irrespective of organizational effort in promoting a just and no-blame culture, the stigma persists with respect to speaking up about errors [ 18 ].

Patient safety incident reporting has become a common practice, but little is known about the feelings of those who commit or witness incidents. Despite the recent debate regarding the use of the term second victim, we are adopting this terminology throughout our research to analyse the consequences of MEs in terms of psychological responses from healthcare staff. Previous research into second victims has mainly been carried out in a single setting, but this study uses reported incidents at a national level drawing from a range of settings. Also, no previous studies, as far as we are aware, have focused only on Medication Administration Errors (MAEs). To our knowledge, none of these studies have used free-text descriptions of reported medication incidents to review the feelings and emotional responses associated with reporting nor text mining as an innovative method for such analysis.

The aim of this study was to investigate negative emotions expressed by healthcare staff in their reported MAE incidents along with the immediate responses they received from their seniors and colleagues after the incident.

Study design and setting

A retrospective study using qualitative descriptive method and text-mining with an inductive content analysis of the incident data related to Medication Administration (MA) reported in England and Wales was done.

Description of the data

The data consists of MA incidents ( n  = 72,390) retrieved from the National reporting and Learning System (NRLS) database based on inclusion criteria: (1) incidents reported to have occurred in England and Wales between 1 January and 31 December 2016, (2) medication incident, (3) administration/supply of medicine from a clinical area, and (4) acute National Health Services (NHS) trust (either specialist or non-specialist). The data included incident reports from all levels of healthcare staff ranging from student nurses to senior-level health professionals who were involved in and who have witnessed the MAE incidents.

Data were acquired from NHS England and NHS Improvement. NRLS is largely voluntary and is the only database that includes all types of patient safety incidents. This study used free-text descriptions of the incidents i.e., healthcare staffs’ descriptions of “what has happened?” or “when the incident occurred?” during the medication process.

Data analysis

First, negative emotional expressions associated with MAEs were defined using the literature and dictionaries (Oxford Learners’ Dictionary, Merriam-Websters’ Dictionary, and Cambridge Dictionary) to define synonyms of the negative emotional expressions (Table  1 ). Second, those expressions were searched from the free-text descriptions of the incidents which were specifically related to MA. For that, The SAS® Enterprise Miner 13.2 and its Text Miner tool were used. Multiple steps were followed for data analysis as described in Fig.  1 . SAS® Text Miner automatically processes the data using ‘text parsing’ i.e., converting unstructured text into a structured form. Text parsing includes tokenization (breaking text into words/terms), stemming (which chops off the end of words reducing words to their stem or root forms), and part-of text tagging (for each word, the algorithm decides whether it is a noun, verb, adjective, adverb, preposition and so on). ‘Text filtering’ was then used to reduce the total number of parsed terms and check the spellings. The English language was chosen for parsing and filtering the text. Using an interactive filter viewer, negative emotional expressions described in the free text were identified and the number of each expression was collected (See Supplementary file  1 ). For the next phase of the analysis, the most common expressions were chosen which are bolded in online-only material 2 (See Supplementary file  2 ).

Analysis process of medication administration incident reports’ free text descriptions

Expressions chosen for analysis were used as a search term in an interactive filter viewer. All the descriptions of the incidents that included those expressions (a total of 1861 incident reports) were collected and read through repeatedly. In the first phase of this analysis, the aim was to define who had experienced the emotional feeling. Most of negative emotions were expressed by patients or relatives (See Supplementary file  1 ). Those descriptions of incidents that included negative emotions expressed by healthcare staff and which were expressed in relation to MAEs ( n  = 93) were then selected for further analysis.

Content analysis was used to analyze the data. The lead author followed an inductive content analysis where the researchers carefully read, organized, and integrated and formed categories, concepts, and themes by comparing the similarities and differences between the coded data [ 19 ]. The lead author read through the data repeatedly and during this process, identified the main theme which is: Emotional expressions of healthcare staff after MAEs. The data were organized into main themes and sub-themes. After the preliminary classification, a co-coder [the last author of this paper] participated in the analysis and read the classification structure and the related data independently. Once thematic saturation was achieved, both researchers analyzed the entire data corpus according to standard thematic analysis techniques [ 20 ]. All authors contributed to the final form of the analysis. Finally, direct quotes were used to support the findings.

Negative emotional expressions of healthcare staff after MAEs

We found 15 different types of negative emotional expressions used including worry, anxiety, annoyance, agitation, stress, unhappiness, distress, concern, anger, upset, shock, sorry, fault, depression, and frustration. These 15 different types of emotions were expressed 1,861 times in the incident reports (See Supplementary file  1 ).

Among those emotional expressions, 12 were exhibited by the healthcare staff and were mentioned 154 times. Only eight of those 12 expressions: worry, upset, agitation, faulty, sorry, concerned, stressed, and distress were expressed by healthcare staff in direct relation to MAEs, the frequency of expression here was 93 times. The data extraction process in presented as a flowchart in Fig.  2 .

Typology and frequency of emotional expressions

The key emotions revealed were further classified into four categories: (1) feeling of fear, (2) feeling of upset, (3) feeling of sadness, and (4) feeling of guilt (Table  2 ).

Feeling of fear

Healthcare staff described their feeling of fear regarding MAEs using four different synonyms i.e., distressed, concerned, stressed, and worried. Staff mentioned how fearful they were when they discovered their mistakes. Distress was revealed in three of the incident reports as expressions of fear of healthcare staff. Usually, MAE incidents were reported either by the error-makers themselves or by those witnessing their errors. One of the staff described the fear felt by her colleague (staff nurse) by reporting how distressed he was after he administered a medication through wrong route (intravenous instead of oral):

“ I was assessing a patient on Ward X when a staff nurse approached me extremely distressed and agitated. He then ran into the utility without explaining what the problem was. I followed him…nurses were present who proceeded to explain that the nurse who approached me had given a patient 2mls of Oramorph [liquid morphine that has to be given orally] intravenously …"

Healthcare staff also expressed the extreme pressure which acted as an important contextual trigger, driving intense the feelings of fear. Another emotion linked to fear was “concerned” which was expressed in 23 cases by healthcare staff after making an error. One of the healthcare staff reported an error (prescribed wrong strength), which the staff realized two hours later and became concerned about it:

" Prescribed TTA (to take away) of ‘Augmentin [Amoxicillin Clavulanate] Duo’125/31 8 ml TDS [three times a day]. As written, this would be a drug error-there is no 125/31 strength of …This was my error, which I realized and became concerned about 2 hours later …"

Stress was expressed in three cases by healthcare staff while reporting the incident; however, this emotion was expressed by staff not as their feelings after MAEs, but as the reason underlying MAEs. These kinds of explanations were found in many incident reports where healthcare staff accepted the error but eventually pointed towards other hidden causes behind the error:

" Gave Clexane [Enoxaparin] 60 mg to wrong patient. Ward extremely busy- heavy workload and was very stressed due to workload …"

Being “worried” was another expression of fear reported in 11 incident reports by healthcare staff. They were found to be worried about several situations such as the health of patient, degree of harm caused by error, associated legal procedures, and their professional career. One staff nurse was worried about the patients’ condition as he did not administer insulin dosage to one of his patients:

" Staff nurse came to me at the end of the shift and stated that he thought that the patients’ insulin was prescribed prn [whenever necessary] and had not given any…I explained he needed to inform the nurse in charge…he was very sincere and worried that he had not given this insulin …"

Feeling disturbed

The feeling of being disturbed was expressed using two synonyms: upset and agitated. They addressed themselves as being upset in 24 incident reports following MAEs committed either by themselves or by their fellow staff. Healthcare staff reported the error made by fellow staff member and described the emotion of his/her colleague as:

" Nurse called me was very upset to explain that she had given wrong treatment to patient …"

Even near miss situations have caused healthcare staff to get emotionally disturbed. Even after apologizing with patient and family, healthcare staff felt upset thinking that if they were not aware of the near miss situation in time, patients’ condition would have been severe:

" SN asked me to do a syringe driver with her for a palliative patient…on drawing up the ketamine driver, myself and SN made a drug error in which we drew 5 times more ketamine than the required dose…The family and patient have been informed of the drug error we made and we gave our sincere apology for our faults…both myself and SN are very upset with the near miss situation and aware that things could have gone very differently …"

Healthcare staff expressed being agitated in two reports after discovering that they had committed MAEs, except in some situations, where staff though agitated denied their mistake by underestimating the severity of the error they made:

" Patient was discharged off the system by the nurse without confirming with medical team/pharmacy that patient was ready to go… Patient left without anti-sickness medication which the team had told her she could have…Nurse was evidently agitated that the incident was being reported and did not understand that she should check with the team before authorizing …"

Some reports revealed extreme negative emotions associated with feelings of upset such as being devastated and questioning one’s own professional competence. The use of such intense and traumatic language can reflect how much the healthcare staff were impacted and even emotionally wrecked after MAE. One healthcare staff after accidentally administering wrong dosage to the patient, reported that the error was entirely his/her own fault:

" Pt px 120 mg on gentamicin on EOMA, I accidentally gave 210 mg in error. This was entirely my fault …The checker confirmed what I had done. I am so devastated about this and really upset I’d made such a mistake…today was just hectic and I lost concentration ..."

Feeling of sadness

Healthcare staff expressed their feeling of sadness at being sorry for the mistake they had made; it was one of the most common negative emotional expressions expressed in 13 cases. Most staff used this to express a sense of remorse after the error. After missing a dose of insulin for a patient, one healthcare staff expressed his/her sadness by stating that he/she is sorry about the incident:

" I am sorry to say that I missed one dose of insulin (at 22.30…) for one of my patients …"

Along with the feeling of sadness, one healthcare staff also mentioned about learning from the error and how he/she have accepted that she was wrong to assume things:

" I was sitting at the desk, staff nurse handed me a tray with intravenous antibiotics and said, here is one because I had given her patient drug chart, I assume it was patients’ medication. I did not take the drug chart with me to the patient and afterwards when staff nurse came with patients’ drug, I realized I have given the wrong drug. I was very upset as I have never done anything in this form before. I always take the drug chart with me to the patient. I am deeply sorry, and this is a massive learning curve for me, I hold my hand up it was wrong to assume this ."

Healthcare staff who had mentioned learning from the error was quite common in many incident reports. However, there were few cases where the staff did not understand the seriousness of the error she has caused:

"… I spoke to the student nurse about the seriousness of her actions, she said sorry; however, I did not feel she understood the seriousness of what she did …"

Feeling of guilt

In 14 incident reporting cases, healthcare staff were aware of their mistakes and the consequences they might have. They expressed their guilt and identified themselves as being at fault and blaming themselves.

" IV flucloxacillin drawn up and checked by myself and staff nurse…administered drug however in error name band/ allergy band not checked. Realized immediately after administration that I had gone to the wrong patient and given the incorrect medication…conversation with senior staff nurse about error. Explained that the error was my fault completely…patient does not appear to have come to any harm …"

However, this emotion was not just expressed following the error, but also as another reason for error attribution. For example, in the report below, a staff member made an error, and blamed herself and phone reception for being muffled:

" I had to hand over two diabetic patients to the 5–8 pm. I rang Ward sister and confirmed this again later. However, patient was not reallocated, and insulin omitted…Ward sister apologized for yesterday missed patient…she said the reception to her phone was muffled and that it was her fault …"

Immediate response from seniors and colleagues

Some of the healthcare staff while reporting their feelings behind MAE incidents also discussed regarding the immediate responses they received from their seniors and colleagues. Healthcare staff explained how their seniors and colleagues responded after they were informed about MAEs. These responses are categorized into two sub-categories: (1) Reassurance and support and (2) Guidance on what to do after an error.

Reassurance and support

In three incident reports, healthcare staff mentioned about the reassurance and positive support they received from their seniors and colleagues after the disclosure of MAEs, about how they tried to handle the situation very calmly without getting angry. This helped them to cope effectively without undue stress and burden. A nurse mentioned that she reassured one of her colleagues who was very disturbed after she gave the wrong medication to her patient:

" Staff nurse by mistake gave the patient wrong medication…. misread the information by being interrupted by a patient and member of staff…. I reassured the staff nurse as she was very upset …"

Even a little support and reassurance and few kind words during the time of MAEs can help the healthcare staff to cope up with the situation effectively. As one member remarked:

" Medication error – digoxin prescribed in two doses (125mcg and 62.5mcg) did not realize and administered…Immediately alerted sister in-charge of ward and contacted doctor. Doctor did not come to the ward but was happy that observations had been recorded…and told us not to worry …"

Guidance on what to do after error

In 11 incident reports, healthcare staff mentioned about receiving advice from their seniors and colleagues regarding the right thing to do after making an error. They have been guided to observe the situation of the patient to ensure that no serious harm would be caused to them:

"… Administered the oramorph in an unlabeled syringe which was in the same tray as a 10ml flush…I discussed the situation with the medical registrar on call who advised me to monitor observations regularly …" "… I spoke to the nurse in charge after the error from the following shift who said that I should speak to the ward manager at the earliest opportunity which I did …"

Furthermore, in cases where healthcare staff neglected to document the incident, a colleague intervened to guide the staff member to follow the protocol. As one staff member described:

"… I discussed the incident with a colleague shortly afterwards. However, I neglected to escalate and correctly document the incident…The aforementioned colleague has since approached me to discuss the incident, further to this I approached and discussed the incident with my ward manager …"

Our study identified four categories of negative emotions expressed in incident reports: feelings of fear, disturbed, sadness, and guilt with various sub-categories. In addition, this study also captured the immediate responses received by healthcare staff after they informed their seniors and colleagues about MAEs including the reassurance, support, and guidance on what to do after an error. Incident reporting by healthcare staff in this study indicated that unintentional harm caused due to MAEs and even near misses can affect the healthcare staff involved in error emotionally, increasing their risk of becoming the second victim of MAEs, confirming previous research [ 9 , 21 ].

A major finding of this study was the negative emotions experienced by healthcare staff after MAEs. Healthcare staff in this study expressed their fear while reporting incidents by using negative emotions such as stressed, distressed, concerned, and worried. They not only blamed themselves for these mistakes, but also considered other additional explanations which, they perceived as causing the error. These kinds of emotions can be related to staff members’ narration of fear and anxiety for patients’ well-being and for their own professional careers [ 22 ]. Similarly, feelings of being disturbed expressed as being upset and agitated were widely mentioned in incident reports. Identical reasons such as realization of the error and thoughts of the possible seriousness of the error and associated issues lay behind emotions. Further, feeling of sadness expressed as being sorry for the mistake made was another most common emotional expression. Also, healthcare staff felt a deep burden of responsibility for their actions. Feelings of being guilty or at fault is one of the risk factors for healthcare staff for becoming the second victim of MEs. It can also cause loss of self-esteem and inculcate a sense of failure and hopelessness. In a similar study by Treiber & Jones [ 22 ], nurses, upon committing even minor errors, expressed raw and painful emotions, regardless of the degree of harm. Nurses can often recall the details of the error and what they felt at that time [ 22 ]. While the lack of any apparent linkage between emotional response and degree of patient harm might appear counter intuitive, one possible explanation might be that healthcare professionals are not well enough supported by their organizations to cope with any form of negative experience. Thus, those affected might develop strong negative emotion [ 23 ].

Making an error might also have serious consequences for disrupting the personal and professional lives of staff, causing personal and moral distress, and affecting the quality and safety of patient care [ 23 ]. It is crucial to pay attention to these emotional expressions as incidents that are sensitive and make an impact, are often remembered, and reflected in the attempt to prevent recurrence. On the other hand, these incidents can unintentionally impose a mental burden on healthcare staff making them second victim [ 2 ]. Our findings confirms that MAEs can generate negative feelings in healthcare staff associated with it, which can endure long beyond the immediate effect.

Research has confirmed a direct relationship between nurse staffing and missed patient care [ 24 , 25 ], revealing poor nurse staffing as a risk factor for MEs along with other organizational factors such as poor working conditions, distractions, and high workload [ 26 ]. Similarly, in this study, reporters mentioned their own actions as a trigger for MAEs along with the above-mentioned factors whereas some reporters explained organizational and environmental conditions and context surrounding the error as reasons to reduce blame. In the absence of support, self-blame seems to assume greater prominence. This can have long-term repercussions for maintaining emotional health and well-being, a major failure of workforce strategy, especially during the pandemic situations.

The current study also found other healthcare authorities responding in several ways after being informed about MAEs. Sometimes, staff may not know what to do after MEs, they might panic and lose control. Thus, adequate support from colleagues and seniors sensitive to these issues may prevent the error-makers from translating further into second victimhood of MEs. How the organization and related individuals responds is clearly linked to the emotional impact the error can have on the healthcare staff who made the error. Appropriate support and guidance from seniors and colleagues have been found to alleviate the suffering, while lack of support has increased their psychological burden [ 27 ]. Some of the healthcare professionals in our study also opted for consulting with their seniors: doctors, colleagues, and mentors after MAEs and reported about how they have received guidance and suggestions, which helped them to cope effectively. Emotional support plays a vital role in restoring faith and confidence among healthcare professionals in patient safety. Support from co-workers and healthcare institution helps the error-makers to retain a sense of control [ 2 ]. Reassurance from seniors and colleagues can also strengthen healthcare staff’s self-esteem and facilitate the correct reporting of MAEs. As is well known, only a fraction of incidents are reported thus deterring the improvement of patient safety with barriers identified as time pressure, fear of the consequences [ 28 ], poor institutional support, lack of feedback, a blame culture, and inadequate training [ 15 ]. Yet, we can still improve patient safety by identifying these barriers. Moreover, while some staff members perhaps too readily assumed responsibility for errors, as reflected in the prominence of self-blame, others demonstrated reluctance, which could be linked to fear of the consequences of MAEs. Furthermore, little is known about the dynamics and consequences of reporting-what prompts some to report and others not to do so. We demonstrate that the emotional expression of staff can be extremely distressing and negatively impact health and well-being of healthcare staff.

Implications for practice

Our findings indicate that immediate negative feelings experienced by healthcare staff after making MAEs can have long-lasting impacts that stretch far beyond the event itself thus potentially traumatizing them and inducing ruminative thoughts, which trigger the memory. The short, medium, and long-term consequences of errors are unknown as yet but could contribute to burnout and other factors associated with intention to leave the profession. Indeed, a negative memory that will stay with them forever, if not handled accurately. They could potentially become second victims of an error, if unable to confront and deal with negative feelings associated with the error. One source of challenge could be stigma related to this making it difficult to continue to work after MAE. Our findings suggest appropriate guidance and support from fellow staff members could help healthcare staff to handle the situation effectively. Therefore, it should be paramount to tailor appropriate support from persons in-charge and colleagues and to promote an open culture where it is understood. Errors can impair mental health of those who are involved, hence, the system triggers surrounding such errors need to be understood and prevented. In addition, more detailed information about these emotions after incidents and their long-term consequences on emotional well-being should be studied in future.

Implications for research

The negative feelings expressed by healthcare staff after MAEs identified in this study could provide the basis for designing an intervention study to support emotionally affected staff in healthcare institutions. It could be helpful to design a support program which recognizes the importance of expressed emotion and its consequences for internationalizing a sense of self blame and victimhood and the long-term repercussions this might have for the mental health and well-being of the health workforce.

Strengths and Limitations

As far as we are aware, this is the first-time text-mining and content analysis have been used to identify negative emotions reported by healthcare staffs’ MAEs, derived from free text in a large national database. A text-mining approach was used for identifying reports that included emotional expressions, as manual data analysis would have been almost impossible for such a big data set and this approach has been recognized to be time-effective in analysing big-data regarding medication incidents (Härkänen et al., 2019). Further, the emotional expressions identified in this study are relatively rare. These descriptive data of emotional expressions nevertheless cast light on the issues related to MAEs. Furthermore, the researchers adhered to the Standards for Reporting Qualitative Research (SRQR) checklist (see the list in Supplementary file  3 ).

However, while analyzing the free-text descriptions, we may have missed some important expressions as this was a pilot methodology we were testing, subjective decisions were made. Similarly, it was very difficult to combine the synonyms of the word used to express the negative emotions which can give rise to ambiguities. For example, in many cases, one single word could either be a verb, or noun or an adjective i.e., words can have different implication [ 29 ]. On the contrary, this study sheds some light upon how important it is to write incident report and to identify the negative emotions of staff, to prevent further consequences from occurring, encourage reporting and put support mechanisms in place. Patient safety incident data is likely to contain some limitations, more specifically, reporting error and bias which will affect the number, type and temporality of reported incidents and data interpretation [ 30 ]. Since reporting is largely voluntary, there are some potential limitations of NRLS being a reliable indicator of exact number of incidents. Nevertheless, increasing number of incidents may reflect an improved reporting culture. Further, the methodology did not allow for the identification of any positive emotions that might have been expressed by healthcare staff when reporting MAE incidents, as only free-text descriptions which included negative emotions were analyzed .From the free text-descriptions, most of the reports were found to be from nurses, however, staff-specific generalizability and scope is limited due to lack of staff type identification in NRLS data i.e., ST01 [ 31 ]. This makes it difficult to precisely quantify the impact and potential benefits of this research.

A wide range of negative emotions was expressed by healthcare staff after reported MA incidents. However, the associated psychological trauma and low mood expressed by healthcare staff represent significant negative impacts underlying reported negative emotions. It is more likely that MAE incidents are under-reported, therefore problems could be much higher in terms of prevalence and magnitude. There was tremendous variation in reports of healthcare staff encountering with MAEs; some reacted in extremely negative ways, whereas the majority expressed little about their feelings. Although many of the incident reporters did not express their feelings in their reports, there is also the possibility of them being affected by the aftermath of MAEs. Several actions were taken by healthcare staff to help cope with the error: which included, seeking guidance, reassuring, and supporting each other. This calls for further efforts from healthcare organizations to support healthcare staff as a matter of routine when encouraging reporting. Though we do know little about the long-term consequences, from what we see in our data, the scarring effect could potentially be considerable. Therefore, support programs need to be co-designed but incentivize to reward reporting without imposing an emotional burden on already overburdened staff. This is vital for error reporting, safety, and ultimately prevention to flourish in the long run. First and foremost, the system needs to promote psychological safety for its users, which our research currently demonstrates.

Availability of data and materials

Data supporting the findings of this study are made available from NRLS/NHS Improvement. However, restrictions apply to the availability of these data. For this current study, these data were used under license, therefore, are not publicly available. Data are however available if contacted to authors (MH, AMR, SM) upon reasonable request and with permission from NRLS/NHS Improvement.

Abbreviations

Medication Error

Medication Administration Error

Medication Administration

National Reporting and Learning System

National Health Services

Standards for Reporting Qualitative Research

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Acknowledgements

The authors want to thank the NHS England and the NHS Improvement Patient safety team for helping the authors through the data acquisition process and refining the data extraction.

This study has been partially supported from the grant received from Sairaanhoitajien Koulultussäätiö and from early-stage researcher position from the University of Eastern Finland for the first author.

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Department of Nursing Science, University of Eastern Finland, Yliopistonranta 1c, Kuopio, Finland

Sanu Mahat & Marja Härkänen

King’s College London: Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, James Clerk Maxwell Building, 57 Waterloo Road, SE1 8WA, London, UK

Anne Marie Rafferty

Department of Nursing Science, University of Eastern Finland, Kuopio, Yliopistonranta 1, 70210, Finland

Katri Vehviläinen-Julkunen

Kuopio University Hospital, Puijonlaaksontie 2, 70210, Kuopio, Finland

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SM conducted the analysis, but all authors (SM, AMR, KV-J, and MH) participated in interpretation of data and in drafting and revising the manuscript critically and gave final approval of the version to be submitted.

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Correspondence to Sanu Mahat .

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Data sharing agreement (Ref: 063.DSA.17) between NHS Improvement and King’s College London dated 22.08.2019 allowed us to use this data. As the data used for this study were voluntarily and anonymously submitted incident reports data (a register study), the need for seeking informed consent from the incident reporters was waived from the ethics committee. The King’s College London ethics committee (LRS-17/18-5150) gave ethical approval for this study in October 2017. Incident data used for this study did not comprise any personal or professional identifiers. Therefore, the anonymity and confidentiality of the data and the persons involved were fully ensured. Further, data handling was made confidential and ethical guidelines were followed.

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Supplementary Information

Additional file 1: supplementary file 1..

Number of incident reports with negative emotional expressions and description about the healthcare staffs’ feeling.  Supplementary file 2. Number of negative emotional expressions related specifically to medication administration incident reports ( n =72,390).  Supplementary file 3. SRQR checklist for reporting qualitative studies.

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Mahat, S., Rafferty, A.M., Vehviläinen-Julkunen, K. et al. Negative emotions experienced by healthcare staff following medication administration errors: a descriptive study using text-mining and content analysis of incident data. BMC Health Serv Res 22 , 1474 (2022). https://doi.org/10.1186/s12913-022-08818-1

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An Analysis of Pandemic-Era Inflation in 11 Economies

In a collaborative project with ten central banks, we have investigated the causes of the post-pandemic global inflation, building on our earlier work for the United States. Globally, as in the United States, pandemic-era inflation was due primarily to supply disruptions and sharp increases in the prices of food and energy; however, and in sharp contrast to the 1970s, the inflationary effects of these supply shocks have not been persistent, in part due to the credibility of central bank inflation targets. As the effects of supply shocks have subsided, tight labor markets, and the rises in nominal wages, have become relatively more important sources of inflation in many countries. In several countries, including the United States, curbing wage inflation and returning price inflation to target may require a period of modestly higher unemployment.

We thank the Peterson Institute for International Economics and the Hutchins Center for Fiscal and Monetary Policy at the Brookings Institution for research support. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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Researchers Plan To Retract Landmark Alzheimer's Paper Containing Doctored Images (science.org) 54 --> 54

Researchers plan to retract landmark alzheimer's paper containing doctored images more | reply login, researchers plan to retract landmark alzheimer's paper containing doctored images.

• Informative
• Interesting

Science working as it should ( Score: 5 , Interesting)

Look, I won't sugar coat this message. A mistake of this magnitude is unforgivable to the scientists who had knowledge of it. The senior author is acting appropriately. Science is working as intended, recognizing mistakes and correcting them.

The alternative world is where you believe everything that is put forth. That path leads to far worse outcomes.

Strongly disagree ( Score: 2 , Interesting)

Strongly disagree.

Publishing is not the same as science. going on 20 years - that is a whole generation of research largely going in the worng direction. Billiions of dollars. 100s of millions dying bereft of dignity.

If this were science as it should be then replication would be as important as the original research. But that is not the way the game is played.

F*** these guys - hanging is not enough for what they did. Instead they got fame and riches.

Re: ( Score: 3 , Insightful)

Clearly written by a non-scientist. If you do research and do not disseminate it, you have done nothing to advance humanity. Publishing is a critical part of science. Sharing your results with others, teaching, is baked into what we, as scientists, do.

The false dichotomy that without this paper hundreds of millions of people would have died with dignity is laughable in its exaggeration.

Hanging not good enough? You want to torture and kill a talented team because one of them was unscrupulous? Please.

Re:Strongly disagree ( Score: 5 , Insightful)

Publishing is a critical part of science.

It is, but science publishing has a strong bias towards new positive results.

It is difficult for scientists to publish negative results and even harder to publish replication research.

The result is that researchers avoid risky research that is likely to fail but could lead to a big breakthrough if successful.

They have even less incentive to do replication.

Since there is pressure to get a positive result, a negative result is faked or p-hacked into a positive result, which is often successful since no one is doing replication.

This is science working as it should: discovering the mistakes and correcting them.

It took years for the fudgery to be exposed and more years for it to be retracted. This was an extremely high-profile paper. Similar fakery in an obscure paper would likely never be noticed.

Re: ( Score: 2 , Flamebait)

Don't blame scientists for lack of negative results papers. Blame yourself for not funding scientists and labs to do replication studies and negative result publishing. How would a negative results journal even work (they already exist btw, or were tried in the past and failed): https://jnrbm.biomedcentral.co... [biomedcentral.com] ) "Checked under the planet and didn't find turtles holding it up." Yeah there's fame and fortune for that.

Re:Strongly disagree ( Score: 5 , Interesting)

Don't blame scientists for lack of negative results papers.

Scientists decide which journals to read and cite.

Scientists do the peer reviews that determine which papers are published in those journals.

Scientists make the hiring and tenure decisions for other scientists based on the publication records in those journals.

It's scientists all the way down.

Blame yourself for not funding scientists

So scientists are mismanaging the money they have, thus the solution is to give them more, with no change in the process or accountability? Why would anyone believe that won't lead to more of the same?

Re: ( Score: 2 )

So scientists are mismanaging the money they have, thus the solution is to give them more, with no change in the process or accountability?

No. You clearly don't know anything about scientists or science funding.

Scientists don't stump up the money, and scientists don't award people with jobs. Those two groups want to see "results", which means new, interesting things which people care about, and ideally get public buzz. No one remembers the names of the second guys to land on the moon.

If scientists some how

Re: ( Score: 1 )

Uh, you said he's wrong then wrote a detailed explanation of why he's right....

Uh, no I didn't.

The problem is structural, and relentlessly ejects scientists from positions of influence that do what you want. And yet somehow you've figured it's the scientists fault collectively.

Then again simplifying a massively complex, nuanced and dynamic issue to being entirely the collective fault of an "other" group is about the level of discourse I'd expect from either of you.

So scientists are mismanaging the money they have,

Scientists have to commit to mismanaging the money before they are given the money. Science is one of the most profitable things there is, but the profits don't go to the scientists, so science is funded by external sources. And those external sources insist on atrocious measures when deciding who to fund (publish or perish type stuff, never do replication studies, etc -- beg us for grant money).

The world would be a vastly different place if the profits generated by the use of Newtonian mechanics, quantum m

"It is, but science publishing has a strong bias towards new positive results."

It doesn't. This is a misunderstanding of statistics. Publishing has a strong bias towards results, as it should.

*Scientists* (in some fields) have a strong bias towards positive results because the little bit of statistics they're taught doesn't tell them how to produce negative results. p > 0.05 is not a negative result, it's an inconclusive one that shouldn't be published. In order to produce an actual negative result you h

Actually, it does have that kind of bias. There are LOTS of papers that never get published, or get published in journals that nobody reads.

Scientists, in every field that I am aware of, have a bias in favor of interesting results. But their bias is minor compared to that of the journal publishers. (And the publishers have a different idea of what "interesting" means.) So if you do a paper that produces expected results, you can't expect to get published in a prestige journal. And perhaps not at all.

Re: ( Score: 3 , Informative)

This was fraud. Fraud to get additional funding, fraud to further their careers, fraud. Plain and simple.

We're not talking about an honest mistake, we're talking about at least one of the authors purposefully, and willingly manipulating images with an agenda. We're talking about others potentially knowing about it, or not policing the actions of their peer group.

When fraud, when purposeful dishonesty is involved, at the very least this should enter a court, similar to criminal court, naturally with a jud

What's happening here is that there has been a MASSIVE uptick in this sort of behaviour.

And what drives the behaviour, eh?

The system is set up to prey on the desperate. Academia is absolutely brutal, for up and coming academics. Gaol is not going to be a disincentive here because no one thinks they will get caught. People are already wrapped up completely in a system they cannot see a way out of.

All you will do with your proposal is increase the prison population slightly at great expense. It won't reduce

Well - I have a PhD in MIS, another ABD in stat, an MBA, a MS in EE, and a BS in Chem E. I would put my "science" credentials against yours any day of the week. Again, imo, our current "science" publishing schema is a perversion, with an emphasis on wiriting papers with statistical significance lacking verification and in most cases pracitical significance.

And don't even get me started on how bad most faculty are at teaching -- which is why most parents even care about academics in the first place. I was

Most of your points are valid, but this is *NOT* science working as it should. Were science working as it should, replication would have immediately be a high priority on several people's agendas. As it is, if they had done a replication, they'd have gotten a pat on the head from their peers, and a kick in the ass from their departments.

This is merely "the best we can do in the current environment". It's a lot better than hiding and obfuscating, but it's not "things working as they should".

• 1 reply beneath your current threshold.

Refunds for journal subscriptions ( Score: 2 )

Nature should provide refunds for the journal subscription for the issue containing the falsified data.

A $240 subscription would need to refund 1/12 or $20 to each subscriber for that year.

It would cost way more than $20 per subscriber to verify any non-theoretical the papers that are selected for publication. If someone fakes a chart, assuming it doesn't present a logical fallacy, it is nearly impossible to verify it without actually carrying out the experiment which would be very costly and time-consuming.

So you want to incentivise Nature to not request article retractions?

I think that's called a malign incentive.

Re: ( Score: 3 )

Huh? I don't think anyone died from this .. did any AB*56 targeting drugs reach market or even clinical testing phase? As for research money .. I doubt billions or even 100 millions were spent on AB*56 research. It seems highly improbable that so much got spent on something nobody else could replicate.

Re:Strongly disagree ( Score: 4 , Insightful)

Yes because 20+ years was wasted going in the wrong direction and the drugs they made gave false hope instead of working on other things that might have actually worked. No matter how you look at it, they set research back 20 years which is directly 20 additional unnecessary years of horrible deaths from Alzheimer's in the future.

What drugs? That was one research line .. AB*56. It didn't set research back much. You act like everyone dropped everything and started looking at AB*56 for 20 years. That's not what happened at all.

Interesting and useful results get replicated. There are LOTS of papers showing that amyloid beta is associated with Alzheimer's. This is a paper looking at a very specific variation, in a mouse model. It is not responsible for pharma's focus on amyloid beta therapies.

Pharma isn't dumb either. They absolutely replicate results before they pour billions of dollars into something.

I was told by several climate scientologists that there are no fake papers.

Perhaps he's really commenting that you can't trust Scientologists?

Re:Science working as it should ( Score: 5 , Insightful)

A mistake of this magnitude is unforgivable to the scientists who had knowledge of it.

Doctoring images does not happen by mistake, it's a deliberate attempt to mislead.

Re: Science working as it should ( Score: 3 )

Sorry, but no. This was supposedly a landmark paper, they not only denied but fought tooth and nail and ruined careers of those that blew the whistle. Moreover this was the source for millions of dollars in research because replication studies are difficult and almost never done because people trust the source.

Doctoring images is an active act of subversion. Itâ(TM)s not a mistake you just make, graphs and images donâ(TM)t doctor themselves.

The first is reprehensible, and deserves severe punishment.

The doctoring of images...I'd need to know the context (which I probably wouldn't understand). I can imagine that being done just to make a point clearer. Remember, sometimes cartoons are used to render an explanation intelligible.

OTOH, in many contexts, the doctoring of images is just as bad as you imply. I think the decision needs to be left to experts in the field, which I definitely am not.

I am a little concerned that you call these "mistakes"? the images were faked, not a case of "ohh, i grabbed the wrong one". This was outright lying. And the author is acting "appropriately" AFTER being called out on it- 18 years later! 18 years of silence 18 years of accolades and prestige from the paper 18 years of fucking up all the research that was based off of these findings- time, money, resources, progress wasted potentially delayed the actual solution that could impact the quality of life and life te

DNA & photo analysis ( Score: 3 )

Demographics ( score: 2 ), doctored images or data ( score: 2 ).

Graphs are visual representation of data. There's no difference.

Images is a bit vague. It's not clear that it's referring to graphs rather than to photos of cells...and those are ALWAYS doctored. They have to be. But you need to explain just what doctoring was done. (Stains, filters, etc. etc.)

Now clearly this was worse than the trivial case, but it's not clear (from the summary) how much worse. Did they substitute one image for another or what.

OTOH, there are good grounds for disbelieving that amyloid bodies cause Alzheimer's independent of this paper. (But good

Oh, Alzheimer's ( Score: 2 )

I thought at first this was the paper with the AI-generated giant rat phallus.

That one is legit.

What would the correction have been? ( Score: 5 , Interesting)

I'm troubled that Nature chose to refuse to publish a correction, assuming there was a reasonable correction that could have been made (e.g. substituting the correct images from a previous draft of the paper). Without full transparency, we'll never really know if the doctoring made a material difference in the results.

I'm troubled that Nature chose to refuse to publish a correction,

The authors have proven unreliable, I'm not surprised they'd be suspicious of giving them a second crack at it with a new set of potentially doctored images. No, the only sensible solution is to flag the paper as fraudulent and move on.

Since their results have been proved to be unreplicatable it is reasonable to assume there is no reasonable correction that could have been made and still maintain the premise they put forth about what causes Alzheimer's.

A new graph would have just delayed everyone's understanding the paper was crap. All the people with names on it but one agreed to withdraw. The guy who faked the graph was the only one putting up a fight about it.

If you want transparency, best get it from people who aren't frauds. And nothing is stopping them from publishing a correction, various websites let you publish anything you like.

Quite meaningless ( Score: 2 )

for which I as the senior and corresponding author take ultimate responsibility

So what is the "ultimate responsibility", are you giving back the grant money to people who would have made better use of them?

Of course not but he feels really bad about it.

Peer review doesn't work ( Score: 2 )

In general I think most scientists are trying to do the right thing and do publish honest papers but we have seen way too many papers go through and have real world impact that got through peer review and every other filter in place and took years to get pulled. Who knows how many other major results across what fields have time bombs waiting to go off?

Being wrong is totally ok. That's science. But faking results should be a serious crime on the same scale as financial fraud with a possibility of fines a

Re:Peer review doesn't work ( Score: 4 )

Being wrong is totally ok. That's science. But faking results should be a serious crime on the same scale as financial fraud with a possibility of fines and jail for a conviction. It is definitely a fraud. Having to maybe retract a paper 20 years later, if caught, doesn't seem like enough disincentive to not fake it.

It is awkward for certain. Especially because now every wrong path might have to prove that they weren't practicing fraud. Stanley Prusiner's work on prions was wildly attacked, and the word fraudulent was used a lot. Many claimed that prions didn't even exist. And here were are, he was not only correct, but won a Nobel prize in 1997 for that work.

So yes, a good chance that prions are involved in Alzheimer's https://www.ucsf.edu/news/2019... [ucsf.edu]

The problem is, the retracted work focused on the wrong one, and tried to make it fit instead of acknowledging it didn't.

Now for my own thoughts on the matter, the research in toto is on the wrong side. Prion infection is simply too late. Similar prion diseases like scrapie or mad cow disease (moo, god dammit!) aren't curable, and my money is on Alzheimer's not being curable, or likely even stoppable once it gets into the brain.

So how are the prions getting into the noggin? And if we find that route of infection, how do we prevent it? Is this an inevitable hard stop for aging, or can there be a vaccine if there is an infection route?

And there is the crux. If a lot of people contract Alzheimer's with no actual cure, that doesn't make money for the drug companies. And that is the bad part of this paper, sending research off in the wrong direction. So we get proof that scientist are as human as politicians. Even with messes like this, my money is on them being hella more ethical

If you have an infection model for Alzhemier's then you could find the cause by transmitting the disease to test subjects (preferably rats or something). If it's prions than it should be able to "infect" tissue samples.

If Alzheimer's *is* a prion disease, then you don't need a source of infection, merely a protein that has a certain probability of folding the wrong way, and a path of replication.

This *would* align with it only being likely to show up in the aged. And I can think of no obvious reason why it's impossible. But there are reasons for believing that it's not the amyloid bodies that cause Alzheimer's.

Hot Take ( Score: 2 )

Hold public executions for all those researchers.

The high number of fake papers ( Score: 2 )

Free speech be damned, science publications are NOT The Onion and the consequences of fraud can be severe. This has reportedly included the sale of dangerous medication, but actual examples are less important than the potential.

Science speech should be placed in a special legal category where both the presentation and publication of fraudulent articles carries penalties, including the automatic rescinding of academic qualifications by scientists indulging in fraud.

There should also be an official rescinding

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