‘You learn better under the gun’: intimidation and harassment in surgical education
Bibliographic record
Abstract
BACKGROUND: Medical literature has documented a high prevalence of intimidation and harassment in the educational context. However, the research has failed to adequately delineate the nature of these phenomena as well as the different ways in which diverse actors perceive the behaviours in question. METHODS: Based on qualitative methodology anchored in a social constructionism framework, how teachers (staff surgeons) and learners (surgical residents) define intimidation and harassment were documented and compared. In addition, teachers' and learners' perceptions of the impact of these behaviours on the learning environment, including their effects on the socialisation of surgeons in training, were examined. FINDINGS: Five group interviews and 22 individual interviews were conducted across 2 university departments of surgery with a total of 22 faculty and 14 resident participants. Interviewees acknowledged the existence of intimidation and harassment, while at the same time rationalising its occurrence. This paradox was encapsulated in participant descriptions using terms such as 'good intimidation'. Our examination of the data helped us to understand that participants sustained the paradox of beneficial intimidation and harassment by rationalising questionable behaviours on 3 specific dimensions, namely: whether an acceptable purpose could be attributed to the perpetrator; whether positive effects of the behaviour existed, and whether there was a perceived necessity for the behaviour. INTERPRETATIONS: Even while their dysfunctional characteristics are recognised, intimidation and harassment are often seen as functional educational tools. The cultural value currently accorded these behaviours needs to be taken into account in educational interventions designed to shift attitudes and actions in this domain.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".