Methods and tools to screen and assess risks for intimate partner violence among women from culturally and linguistically diverse backgrounds in six high-income countries: A scoping review
Bibliographic record
Abstract
Abstract Purpose The purpose of this review is to collate literature on approaches to screening women from culturally and linguistically diverse (CaLD) backgrounds for experiences of intimate partner violence (IPV) and assessing risks, with a view to identifying examples of best practice and research gaps. Methods A scoping review methodology was adopted. Medline (Ovid), Embase, CINALH and CENTRAL databases were searched, with supplementary searches for grey literature. Results were independently screened by two reviewers. Studies were included if they focused on women from CaLD backgrounds living in Australia, Canada, Ireland, New Zealand, United Kingdom or United States of America being screened/assessed in a health setting in relation to IPV. Data on study characteristics and key findings were extracted and critical appraisal of study quality was performed. Results A total of n = 1,320 results were yielded. After deduplication, the titles and abstracts of n = 846 studies were screened. A total of n = 5 studies were included in the final analysis, and four screening or risk assessment tools/methods were assessed (Danger Assessment for Immigrant Women, Safe Start, Index of Spouse Abuse and Southern Asian Violence Screen). Conclusions Given the documented barriers to migrant help-seeking, screening and risk assessment has an important role to play in ensuring that women from CaLD backgrounds are linked into appropriate IPV support services in a timely manner. However, there is very limited evidence to demonstrate that existing screening/risk assessment tools and strategies meet the specific needs of CaLD populations, and more attention needs to be given to intersectional experiences of violence.
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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 | 0.042 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.033 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| 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".