‘Poorly relaxed women’: A situational analysis of pelvic examination learning materials for medical students
Bibliographic record
Abstract
BACKGROUND: Certain clinical pelvic examination (PE) teaching methods have been critiqued for prioritising student learning over patient autonomy and for not accurately representing diverse patient communities. As such, patient-centred and culturally competent approaches to the PE may need further emphasis in the medical curriculum-in particular, in content delivered to students before patient interaction. Classroom materials serve as students' first exposure to the sensitive procedure. This research explores how patients are represented in these materials. METHODS: A situational analysis was conducted on 10 purposively sampled PE learning materials for the 2019/20 academic year from five undergraduate medical schools in Canada. Situational analysis focuses on analysing discourse but is epistemically aligned with post-structuralism (most notably Foucault's theories involving discursive power) and allows for specific consideration of 'silences' in the data. Collected data were analysed using cartographic approaches according to this methodology, with particular attention paid to the tenets and frameworks of patient-centred and culturally competent care. RESULTS: Overall, content in these materials misrepresented and under-represented patients. Materials contained both outdated and unnecessarily sexualised language, in addition to a lack of patient diversity. Clinical authority was often centred over patient agency, and several updated PE techniques known to improve patient experience were absent. Patient-centred and culturally competent approaches were therefore inadequately highlighted in most of the materials. CONCLUSIONS: Depictions contained in these materials may be perpetuating stereotypes and biases in medicine and may be working to maintain teaching practices that cause harm to patients (standardised and regular) who students interact with in both clinical and educational settings. Efforts may be needed to improve classroom materials on the PE so that they more adequately centre patients and provide opportunities to discuss culturally competent approaches to the procedure that (i) may not be covered in other parts of the PE curriculum and (ii) can reduce known health disparities.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".