Preparation of Australian and Spanish nursing students for intimate partner violence
Bibliographic record
Abstract
Objective : Throughout the world intimate partner violence (IPV) is a significant issue and it is important that nurses contribute to policy development, as well as to the nursing care of families. Nurses are uniquely positioned to identify, and support women experiencing IPV. For them to contribute to policy development, they need firstly to develop a better understanding of the issue and to their role in addressing it. This study explored and compared perceptions, attitudes and knowledge of IPV of nursing students in Australia and Spain. Methods : Students from all levels of the nursing programs in both countries participated in focus groups and a follow up survey exploring their understanding of, and attitudes towards IPV. The data from the focus groups was analysed thematically and the quantitative data from the survey statistically. Results : Spanish nursing students had significantly more positive/comprehensive views about the role nurses have in managing IPV, had a stronger view about the nurses’ role and that they were more prepared. Although the Australian and Spanish participants were not identical, for example, the Australian sample was predominantly female and over the age of 35, these factors do not explain why the difference. The study was only undertaken in one Australian University and one Spanish university so results cannot be generalised to either country. Conclusions : The findings suggest that there may be much more that could be done to prepare nurses to deal with issues of IPV and to take a lead role in recommending policy changes worldwide.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".