Medical and Nursing Students' Knowledge and Attitudes Toward Violence Against Women in India
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to explore the knowledge and attitudes towards violence against women among fourth (final) year baccalaureate nursing students and fifth (final) year medical students from two distinct educational institutions in India. METHODS: Data were collected from 440 students using two questionnaires: the Student Exposure to Woman Abuse Questionnaire (SEWAQ), and the Inventory of Beliefs about Wife Beating (IBWB). Results were analysed based on gender, profession, and educational institution. FINDINGS: Nursing students believed that they had received more classroom preparation and practical skills to better prepare them to assist abused clients than male and female medical students. Only 38% of the participants believed that they had acquired classroom knowledge on woman abuse through their respective educational programs, whereas 43% thought they had practical skills to care for victims. All participants were sympathetic toward abuse victims, but demonstrated varying attitudes about the justification for abuse against women, help given to victims, punishment of the offender and the effect of woman abuse. Female medical students believed more strongly than males and nursing students that wives do not gain from being beaten. CONCLUSIONS: Congruent with existing literature, the study demonstrated that health care students in India do not receive sufficient training, practical skills and classroom knowledge to effectively manage abuse against women.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".