Intimate Partner Violence Risk Assessment: A Primer for Social Workers
Bibliographic record
Abstract
Social workers are likely to encounter intimate partner violence (IPV) survivors and/or perpetrators within their practice due to the prevalence of this social issue and the negative health and mental health consequences resulting from it. IPV risk assessments can be utilised by social workers in multiple service settings. A recent meta-analysis provided information on the IPV risk assessment instruments with the greatest predictive accuracy, but social workers need to know the most appropriate IPV risk assessment tools for use in their particular practice settings. Therefore, this paper provides social workers with summary information on the four risk assessment instruments that have the highest predictive accuracy—the Danger Assessment, the Spousal Assault Risk Assessment, the Ontario Domestic Assault Risk Assessment, and the Domestic Violence Screening Inventory. For social workers unable to use validated risk assessments, a summary of the risk factors is provided with a focus on opportunities for change within violent relationships. Finally, recommendations for which IPV risk assessment to use in various social work practice settings are outlined. The use of IPV risk assessment should be situated within an evidence-based practice framework, taking into account the best evidence of risk for future harm, clinical expertise and client self-determination.
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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 | 0.051 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.013 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".