Conformity, obedience, and the Better than Average Effect in health professional students
Bibliographic record
Abstract
Background: Compliance, through conformity and obedience to authority, can produce negative outcomes for patient safety, as well as education. To date, educational interventions for dealing with situations of compliance or positive deviance have shown variable results. Part of the challenge for education on compliance may result from disparities between learners' expectations about their potential for engaging in positive deviance and the actual likelihood of engaging in positive deviance. More specifically, students may demonstrate a Better Than Average Effect (BTAE), the tendency for people to believe they are comparatively better than the average across a wide range of behaviours and skills. Methods: Four vignettes were designed and piloted using cognitive interviews, to investigate the BTAE. Conformity and obedience to authority were each addressed with two vignettes. The vignettes were included in a survey distributed to Canadian health professional students across multiple programs at several different institutions during the Winter 2019 semester. Self-evaluation of behaviour was investigated using a one-sample proportion test. Demographic data were investigated using logistic regression to identify predictors of the BTAE. Results: Participants demonstrated the BTAE for expected behaviour compared to peers for situations of conformity and obedience to authority. Age, sex, and program year were identified as potential predictors for exhibiting the BTAE. Conclusions: This study demonstrated that health professional students expect that they will behave better than average in compliance scenarios. Health professional students are not exempt from this cognitive bias in self-assessment. The results have implications for education on compliance, positive deviance, and patient safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".