“The Average Counsellor Wouldn’t Know”: Exploring How General Health Practitioners Understand and Respond to Domestic Violence
Bibliographic record
Abstract
BACKGROUND: Individuals experiencing and perpetrating intimate partner violence (IPV) are frequently in contact with general health and mental health services. Health service providers, including nurses, thus have a key role in identifying and responding to initial indicators of IPV risk. PURPOSE: The present study provides descriptive information about current assessment and intervention practices of health and mental health service providers when patients are presenting with concerns about IPV. METHODS: A secondary data analysis of interviews with general health practitioners (n = 17) were coded and dominant themes analyzed through thematic analysis. RESULTS: The present study uncovered ways in which IPV-related risks are, and are not, recognized and responded to. A metaphorical visual display in the form of a "domestic violence supply room" depicts the level of access and degree of competency described by practitioners in respective areas of practice. Within reach for all practitioners is the knowledge of factors that increase risk and vulnerability to IPV. Out of reach is a comprehensive understanding of the needs of children and perpetrators as well as the consistent ability to consider intersectionality and be reflexive when working with culturally and linguistically diverse populations. The step ladder to improved IPV response, including formal supports such as training and procedures, is frequently described as lacking. CONCLUSIONS: A consistent and empirically supported approach to IPV assessment and response is rare to find across generalist service provision. Although service providers possess basic knowledge of risk factors, organizational direction is needed to allow providers to address IPV confidently and effectively.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".