Canadian medical student knowledge and attitudes toward female genital mutilation
Bibliographic record
Abstract
Background: Despite female genital mutilation/cutting (FGM/C) practices being an illegal form of gender-based violence in Canada, this practice impacts many Canadians. Lack of education and training among Canadian health-care providers has resulted in systematic barriers to care. Awareness and FGM/C-related education among Canadian health-care providers must be urgently assessed. Methods: Canadian medical students were recruited to complete an anonymous survey via E-mails distributed through their schools' student organization between January and March 2021. We evaluated student understanding of FGM/C, attitudes toward medicalization and legislation, and prior clinical experience using multiple choice, Likert scale, and open-response questions. Results: Respondents (n = 135) performed poorly on knowledge assessment questions (mean percent correct <50%). Only 10.4% of respondents indicated knowing how to involve appropriate authorities when necessary, and most never evaluate FGM/C in patient history (86.7%) or clinical examination (57.1%). Subgroup analysis revealed that prior education significantly improved knowledge scores and influenced students' behaviors and attitudes. About 92.2% of respondents supported the integration of FGM/C curricula in undergraduate medical education. Discussion: This study reveals that Canadian medical students have a poor understanding of FGM/C and are not prepared to identify affected patients or intervene when necessary. These results provide rationale for the implementation of FGM/C-learning modules in undergraduate medicine.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".