Engaging domestic abuse practitioners and survivors in a review of outcome tools – reflections on differing priorities
Bibliographic record
Abstract
Researchers often develop and decide upon the measurement tools for assessing outcomes related to domestic abuse interventions. However, it is known that clients, service providers and researchers have different ideas about the outcomes that should be measured as markers of success. Evidence from non-domestic abuse sectors indicates that engagement of service providers, clients and researchers contributes to more robust research, policy and practice. We reflect on what we have learnt from the engagement of practitioners and domestic abuse survivors in a review of domestic abuse measurement tools where there were clear differences in priorities between survivors, practitioners and researchers about the ideal measurement tools. The purpose of this reflective article is to support the improvement of future outcome measurement from domestic abuse interventions, while ensuring that domestic abuse survivors do not relive trauma because of measurement.
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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 | 0.095 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".