Gender-based violence: a five-country, cross-sectional survey of health and social care students’ experience, knowledge and confidence in dealing with the issue
Bibliographic record
Abstract
Health and social care professionals are well placed to identify and respond to those affected by gender-based violence; yet students across a range of health disciplines describe a lack of knowledge, preparation and confidence in dealing with the issue. Our study aimed to explore health and social care students’ perceptions of their own knowledge and confidence on the subject of gender-based violence, recollections of gender-based violence learning opportunities through university and clinical placements, and opinions about the content of future e-learning curricula on the subject. We designed and implemented a multinational, cross-sectional survey across six universities from five countries: Australia, Canada, England, New Zealand and Scotland. Responses were obtained from 377 students across seven health and social care disciplines. Principally, the study found that students were underprepared in their professional programmes in terms of dealing with gender-based violence. Many students had witnessed or heard about cases of gender-based violence on clinical placement, but reported feeling generally unconfident in dealing with the issue. Regarding future e-learning, students indicated that content should be inclusive and relate directly to clinical practice. We argue that there is a universal need for health care education programmes to include the issue of gender-based violence in curricula.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".