Evaluation of an acute trust Domestic Abuse Coordinator role: Impact findings and a budget impact model
Bibliographic record
Abstract
Domestic Abuse Coordinators (DACs) work strategically across National Health Service (NHS) hospital and other off-site clinical settings to support clinical staff in domestic abuse enquiry and response, and to co-lead the development and implementation of effective clinical policies and procedures for the management of domestic abuse and the support of survivors. Drawing on data from a large NHS acute trust in central London, we analyse the impact of the DAC role in increasing the rate of referrals of high-risk domestic abuse cases, and generate plausible estimates of the budget impact of the DAC role in respect of costs accrued to NHS trusts. Using eight quarters of clinical data and an interrupted time series design, we find that evidence that implementation of a DAC role is linked with an increase in the rate of high-risk referrals of between 18% and 21% per quarter, indicating improved responses to victim-survivors at highest risk of imminent harm. Under a range of reasonable assumptions, initiation of the DAC role is shown to be cost-saving to an employing acute trust. Future work should seek to quantify the direct impacts to survivor health and wellbeing of the implementation of the DAC role.
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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 | 0.058 | 0.167 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".