Experiences of pressure to conform in postgraduate medical education
Bibliographic record
Abstract
BACKGROUND: Perception of pressure to conform prevents learners from actively participating in educational encounters. We expected that residents would report experiencing different amounts of pressure to conform in a variety of educational settings. METHODS: A total of 166 residents completed questionnaires about the frequency of conformity pressure they experience across 14 teaching and clinical settings. We examined many individual characteristics such as their age, sex, international student status, level of education, and tolerance of ambiguity; and situational characteristics such as residency program, type of learning session, status of group members, and type of rotation to determine when conformity pressure is most likely to occur. RESULTS: The majority of participants (89.8%) reported pressure to conform at least sometimes in at least one educational or clinical setting. Residents reported higher rates of conformity during informal, rather than formal, teaching sessions, p < .001. Also, pressure was greater when residents interacted with higher status group members, but not with the same or lower level status members, p < .001. Effect sizes were in the moderate range. CONCLUSIONS: The findings suggest that most residents do report feeling pressure to conform in their residency settings. This result is consistent with observations of medical students, nursing students, and clerks conforming in response to inaccurate information within experimental studies. Perception of pressure is associated with the setting rather than the trainee personal characteristics.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".