Is Specific Consent for Educational Sensitive Exams on Anesthetized Patients Necessary? Arguments for and Against From Canadian Medical Students
Bibliographic record
Abstract
• Most Canadian medical students support consent for educational sensitive exams under anesthesia. • A minority of students do not support consent for educational sensitive exams under anesthesia. • Reasons for ensuring specific consent include patient rights, autonomy, respect, and integrity. • Some students worried that consent requirements could lead to missed educational opportunities. • Confusion exists regarding the moral basis of consent for sensitive exams under anesthesia. Discussions related to the importance of seeking specific consent for sensitive (e.g., pelvic, rectal) exams performed on anesthetized patients by medical students have been growing. The perspectives of Canadian medical students on this topic have never been sought. An anonymous questionnaire related to experiences and beliefs regarding consent and sensitive exams under anesthesia (EUAs) within medical training was distributed to medical students across Canada. Of 17 medical schools in Canada, 6 confirmed distribution, 8 schools expressed an intention to distribute but did not confirm whether distribution had occurred, and 3 did not share the questionnaire. 134 respondents answered at least 1 question. Canadian medical students who had begun clerkship or previous students enrolled within the last 5 years were eligible. Our data indicates that, of Canadian medical students surveyed, the majority (72%) believe that specific consent for educational sensitive EUAs is a moral requirement, while 20% are unsure, and 8% do not share this belief. Respondents offered a variety of reasons why specific consent is necessary, including patient rights, autonomy, respect, and integrity, as well as concerns related to nonconsensual exams being abusive or constituting assault. Some respondents noted that consent is already implied, is unnecessary because of clinical benefit, or requiring it could lead to missed educational opportunities, especially for male and/or racialized medical students. These results indicate that while most respondents support specific consent for educational EUAs, confusion regarding the purpose of these exams and the moral basis of specific consent exists in training.
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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 | 0.048 | 0.146 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.029 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 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".