Consent and Educational Sensitive Exams on Anesthetized Patients: Experiences of Medical Students Across Canada
Bibliographic record
Abstract
OBJECTIVES: This study investigates experiences of medical students across Canada related to consent for educational sensitive (i.e., pelvic, rectal) exams under anesthesia (EUAs). METHODS: A bilingual online questionnaire was developed and distributed to medical students across Canada. RESULTS: Of 134 respondents, 63% had performed a pelvic EUA, 35% a rectal EUA, and 11% another sensitive EUA during their training. For those who had performed pelvic EUA, 28% were unsure if consent had taken place, 26% reported no specific consent, 20% reported specific consent, and 25% had mixed experiences of consent. For rectal EUAs, 48% reported no specific consent, 37% were unsure if consent had taken place, 13% reported that there had been specific consent, and 2% reported mixed experiences. Most respondents were uncomfortable (36%) or not sure if they were comfortable (32%) with how the consent process was handled for student pelvic EUAs; 31% were comfortable. In open-ended responses, respondents described experiences related to variability, discomfort, and authority. CONCLUSIONS: Non-consensual educational sensitive EUAs continue to take place in medical training across Canada, although practices of consent are highly variable. The majority of respondents reported being uncomfortable or unsure if they were comfortable with how consent for educational sensitive EUAs was practised during their training, and some respondents struggled to express their discomfort given the power dynamics at play.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".