‘Poorly relaxed women:’ A situational analysis of pelvic exam learning materials for medical students
Bibliographic record
Abstract
This thesis analyzes how patients are represented in pelvic exam learning materials used by Canadian medical students. Adopting a postmodern form of qualitative analysis and drawing on conceptual frameworks of discourse theories, the social construction of the biomedical body, and relational autonomy, I demonstrate that these documents present a narrow depiction of bodies, identities, experiences, and patient choice related to the procedure. Through these limited depictions, the documents uphold normative ideologies of the body and position providers as authorities over patients on when and how the procedure will occur. The pelvic exam has historically been a loaded procedure, one that continues to pose challenges in the realms of patient care and medical training. Textbook and training manuals present just one aspect of how medical students learn to perform the exam; however, the representations in these documents matter. I suggest that the depictions contained in these materials are harmful to medical students – and ultimately patients – by not adequately preparing future clinicians to provide empowering, trauma-informed, and culturally sensitive care that meets the needs of people who receive pelvic exams. I conclude by suggesting detailed improvements to the learning materials and medical curricula more generally that work towards an ethic of inclusion in medical education
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".