How medical learners and educators decide what counts as mistreatment: A qualitative study
Bibliographic record
Abstract
INTRODUCTION: The mistreatment or abuse (maltreatment) of medical learners by their peers and supervisors has been documented globally for decades, and there is significant research about the prevalence, sequelae and strategies for intervention. However, there is evidence that learners experience maltreatment as being less clear cut than do researchers, educators and administrators. This definitional ambiguity creates problems for understanding and addressing this issue. The objective of this study was to understand how medical learners and educators make sense of less-than-ideal interactions in the clinical learning environment, and to describe which factors influenced their perception that the encounter constituted maltreatment. METHODS: Using constructivist grounded theory, we interviewed 16 medical students, 15 residents or fellows, and 18 educators associated with a single medical school (n = 49). Data collection began with the most junior learners, iterating with analysis as we progressed through the project. Constant comparative analysis was used to gather and compare stories of 'definitely', 'maybe' and 'definitely not' maltreatment across a variety of axes including experience level, clinical setting and type of interaction. RESULTS: Our data show that learners and educators have difficulty classifying their experiences of negative interpersonal interaction, except in the most severe and concrete cases. While there was tremendous variation in the way they categorised similar experiences, there was consistency in the elements drawn upon to make sense of those experiences. Participants interpreted negative interpersonal interactions on an individual basis by considering factors related to the interaction, initiator and recipient. CONCLUSIONS: Only the most negative behaviour is consistently understood as maltreatment; a complex process of individual sense-making is required to determine the acceptability of each interaction. The differences between how individuals judge these interactions highlight an opportunity for administrative, research and faculty development intervention.
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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 | 0.033 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".