Referral to Health and Social Services for Intimate Partner Violence in Health Care Settings
Bibliographic record
Abstract
Efficient and coordinated health care responses to intimate partner violence (IPV) are essential, given that health care settings are a major entry point for abused women who seek professional services. However, there is a lack of evidence on how IPV referrals are effectively made within health care settings. In order to help program planners and providers across sectors to address the complex and chronic issue of IPV, a greater understanding of the post-IPV identification referral process is essential. A scoping review of the evidence on IPV referral programs and processes in health care settings was undertaken to provide an overview of the state of evidence and identify pertinent gaps in existing research. The scoping review identified 13 evaluative studies and 6 qualitative, primarily nonevaluative studies that examined IPV referral programs and processes. Evaluative studies involved a variety of designs and IPV referral outcomes. Rich descriptions of barriers and facilitators to seeking referrals by victims and making referrals by health care providers emerged from the evaluative and qualitative studies, but were explored more in depth in the qualitative studies. This scoping review provides guidance on what is currently known about IPV referral programs in health care settings and provides a starting point for further research on effectiveness of referral processes.
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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 | 0.028 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".