Why we should view the decision of medical trainees to cheat as the product of a person‐by‐situation interaction
Bibliographic record
Abstract
BACKGROUND: Cheating during medical training is a delicate subject matter with varying opinions on the prevalence, causes and gravity of cheating during training. PROPOSED FRAMEWORK: In this article, the authors suggest that the decision to cheat is best viewed as the product of a person-by-situation interaction rather than indicating inherent dishonesty and/or extrinsic motivation in those who participate in cheating. This framework can explain why individuals who would typically default to honesty may participate in cheating if there is perceived justification for cheating and where situational variables, such as ease of cheating, rewards for cheating and perceived risk associated with cheating, make the decision to cheat appear rational. DISCUSSION: They discuss why the impression that there is a culture of cheating can provide perceived justification for medical trainees to cheat if they have the opportunity. They then describe how aspects of medical training and assessment may enable or hinder cheating by trainees. Consistent with the person-by-situation interaction framework, they contend that our response to cheating should include interventions directed at both the person who cheated and situational variables that enabled cheating. Recognising that some forms of cheating may be widespread, difficult to detect and contentious (such as the creation and use of exam reconstructs), their proposal for dealing with suspected and pervasive cheating is to identify and target enabling variables such that the decision to cheat becomes less rational. Their hope is that in so doing, we can gradually nudge trainees and the culture of medical training towards honesty.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".