Exploring Factors Shaping Primary Health Care Readiness to Respond to Family Violence: Findings from a Rapid Evidence Assessment
Bibliographic record
Abstract
Abstract Purpose Family violence (FV) is defined as any situation where an individual employs abusive behaviour to control and/or harm a former or current spouse, non-marital partner, or a member of their family. The health consequences of FV are vast, including a wide range of physical and mental health conditions for individuals experiencing violence or survivors, perpetrators, and their children. Primary health care (PHC) is recognized as a setting uniquely positioned to identify the risk and protective factors for FV, being an entry point into the health care system and a first, or only, point of contact for families with professionals who can facilitate access to specialist care and support. Methods A rapid evidence assessment of empirical studies on FV interventions in PHC was conducted to examine outcomes of effective FV interventions that promote identification, assessment, and care delivery within diverse PHC settings, factors shaping PHC provider and system readiness, and key intervention components that are important for sustaining PHC responses to FV. After completing data extraction, quality appraisal, and a hand search, a total of 49 articles were included in data synthesis and analysis. Results Several FV interventions that include multiple components such as, screening and identification of FV, training of PHC providers, advocacy, and referrals to supports, have been rigorously tested and evaluated in diverse PHC settings in rural and urban areas including primary care/family medicine practice clinics and community PHC centers. These interventions have demonstrated to be effective in identifying and responding to violence primarily experienced by women. There is a dearth of FV interventions or programs from empirical studies focused on men, children, and perpetrators. Additionally, provider and system readiness measurement tools and models have been implemented and evaluated in PHC specifically to assess physician or the health care team’s readiness to manage FV in terms of knowledge and awareness of FV. The findings highlight that there is no clear or standardized definition of provider or system “readiness” in the literature related to FV responses in PHC. Further, the findings revealed four key intervention components to facilitate PHC provider and organization readiness to address FV: (1) multidisciplinary teamwork and collaboration, (2) improving provider knowledge on the social and cultural determinants impacting FV, and (3) embedding system-level supports within PHC. Conclusions FV is a serious public health concern and PHC providers have a vital role in early detection of FV and the poor health outcomes associated with violence A focus on comprehensive or multi-component FV interventions are more likely to change provider behavior, and would allow for safe, confident, and professional identification and assessment of FV within PHC.
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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 | 0.146 | 0.380 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.027 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".