Women’s Health Themes Across the Undergraduate Medical Curriculum at the University of British Columbia
Bibliographic record
Abstract
OBJECTIVE: The overall objective of the project was to determine whether the current MD undergraduate curriculum at the University of British Columbia (UBC) met the minimum competencies in women's health according to available guidelines. METHODS: Ovid and MEDLINE were searched for information on women's health topics in medical undergraduate curricula. The Association of Professors of Obstetrics and Gynaecology (APOG) and the Association of Professors of Gynecology and Obstetrics (APGO) medical student objectives were used as a framework for evaluation of the UBC curriculum. The APGO women's health care competencies for medical students were also compared with these objectives. A comprehensive review ouate of the medical curriculum at UBC was then carried out to analyze whether, when, and where the APOG and APGO objectives were met. RESULTS: Of the 93 women's health competencies outlined by APGO, only two were not formally addressed in the UBC curriculum. Almost two thirds (60 of the 93) of the competencies are covered in the obstetrics and gynaecology third-year clerkship, which is just one of the 14 teaching settings available for potential coverage of the women's health care competencies. CONCLUSION: Topics in women's health appear to be well addressed by the UBC medical undergraduate curriculum, although this review was unable to determine whether and how extensively these topics were actually delivered.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".