The Effectiveness of Targeted Interventions for Children Exposed to Domestic Violence: Measuring Success in Ways that Matter to Children, Parents and Professionals
Bibliographic record
Abstract
The ultimate goal of trials is to identify interventions that can benefit individuals in the future. It is crucial, therefore, that they measure outcomes that reflect the priorities and expectations of those using the interventions. We consider this issue in relation to trials of interventions for children exposed to domestic violence and abuse (DVA). To explore this, we drew on data collected as part of a larger study to consider whether the types of outcomes measured in clinical trials reflect: (1) the perceived benefit of interventions reported in qualitative evaluation studies; and (2) the views of parents, professionals and young people as to what constitutes a ‘good outcome’. We found that trials most frequently evaluated changes in children's symptoms and disorders, whereas children and parents, along with practitioners, had broader concepts of success that extended beyond narrow health‐focused outcomes. A number of studies measured other types of outcomes, although there was inconsistency in the types of outcomes that were measured. Based on these findings, we discuss the need to reach consensus on an expanded set of outcomes to be measured in child‐focused DVA trials. This will mean that the effectiveness of interventions is judged against outcomes that are important to those who use interventions. It will also facilitate greater consistency in outcome measurement across studies, thereby enhancing the quality of evidence in this emerging field. Copyright © 2015 John Wiley & Sons, Ltd. ‘Need to reach consensus on an expanded set of outcomes to be measured in child‐focused DVA trials’ Key Practitioner Messages In order to adequately assess what works to reduce the impact of domestic violence on children, researchers should: Work with stakeholders to understand which outcomes are important to them. Seek consensus about a standardised set of outcomes to be measured and reported in all trials. Ensure that the success of interventions is measured against these outcomes.
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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 | 0.450 | 0.633 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".