Advancing nursing's response to the wicked problem of intimate partner violence
Bibliographic record
Abstract
Advancing nursing's response to the wicked problem of intimate partner violenceAs a wicked problem, intimate partner violence (IPV) is complex, multi-dimensional and global.It is influenced by intersecting social, environmental, and political factors.Therefore, it requires a multifaceted response to minimize the health, economic and social burdens associated with experiences of violence.Given the complexity of the problem of IPV, a 'one size fits all' approach to assessment and response is no longer sufficient.Our efforts must now be focused on advancing nurses' skills to deliver care that is tailored to meet the diverse needs of women and other groups at disproportionate risk of IPV.These nurse-led interventions then need to be strategically implemented and sufficiently resourced within care contexts, where cultural, physical and emotional safety are prioritized.In this Special Issue we use selected examples of the included articles to illustrate how internationally, nurses are leading the development, evaluation, and implementation of healthcare responses to identify and respond to individuals experiencing IPV.However, because of the complexity and tenacity of the problem of IPV, we cannot stand still.Nursing needs to evolve and adapt.With this in mind, we focus on advances in the following areas: improving nurse education on IPV; personcentred and trauma-and violence-informed care; healthcare organization's initiatives to tackling IPV.
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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 | 0.018 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".