Preparedness of Australian and British nurses and midwives about domestic violence and abuse
Bibliographic record
Abstract
BACKGROUND: Domestic violence and abuse (DVA) is a major health problem that affects individuals across the world. Nurses, midwives and healthcare providers need to be confident and competent in identifying and responding to DVA. AIMS: To measure current levels of knowledge, opinions and preparedness towards DVA and how it is managed by registered nurses and midwives residing in Australia and the UK. METHODS: A cross-sectional study design was used. Data were collected using the Physician Readiness to Manage Intimate Partner Violence Survey (PREMIS) measuring the perceived preparation and knowledge, actual knowledge, opinions and practice issues. Australian data were collected in 2018 and UK data were collected in 2017-2018. Descriptive and inferential statistics were used to analyse the data and differences in knowledge and attitudes of British and Australian nurses. FINDINGS: Nurses and midwives (n = 368; 130 from Australia; 238 from the UK) responded to the survey. Minimal previous DVA training was reported by the participants. Participants had minimal knowledge about DVA, though had a positive attitude towards engaging with women experiencing DVA. DISCUSSION: Most participants felt unprepared to ask relevant questions about DVA and had inadequate knowledge about available resources. Australian participants scored better than British participants; however, the mean difference in all aspects remained statistically insignificant. CONCLUSION: Australian and British nurses and midwives have a positive attitude towards women experiencing DVA; however, the knowledge and skills to support women experiencing DVA are limited. IMPLICATIONS FOR NURSING POLICY: Nursing institutions should develop strategic policies regarding mandatory preparation and training of nurses for domestic violence assessment and management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".