Lifetime intimate partner violence exposure, attitudes and comfort among Canadian health professions students
Bibliographic record
Abstract
BACKGROUND: Intimate partner violence (IPV) is a widespread public health problem and training of health professions students has become common. Understanding students' prior knowledge, attitudes and personal exposure to IPV will aid educators in designing more effective curriculum. As interprofessional educational efforts proliferate, understanding differences across disciplines will be critical. FINDINGS: Students in the schools of Medicine, Nursing and Rehabilitation at a university in Ontario attend an annual daylong interprofessional IPV training. To measure perceived role and comfort with IPV and prior personal exposure, we administered a brief Likert scale survey to a convenience sample of students over three years. 552 students completed the survey; the overall response rate was 73%. The majority (82%) agreed that it was their role to intervene in cases of IPV; however Rehabilitation students expressed lower overall comfort levels than did their peers in other schools (p < .0001). Gender, age and prior training on the subject were not significant predictors of comfort. Seven percent reported lifetime IPV and one-fifth had witnessed IPV, but these exposures did not predict comfort in adjusted logistic regression models. CONCLUSION: While the majority of professional students believe it is their role to address IPV in clinical practice, comfort level varied significantly by field of study. More than one fifth of the students reported some personal exposure to IPV. However this did not impact their level of comfort in addressing this issue. Educators need to take students' preexisting attitudes and personal exposure into account when planning curriculum initiatives in this area.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".