Outcome Measures for Evaluating Intimate Partner Violence Programs Within Clinical Settings: A Systematic Review of the Literature
Bibliographic record
Abstract
BACKGROUND: Multiple intimate partner violence (IPV) identification and assistance programs have been implemented across clinical settings. The results of these studies are inconclusive and frequently conflicting, resulting in clinical uncertainty and controversy regarding the merits of IPV identification and assistance programs. We aimed to describe the choice of outcome measures used in previously published randomized trials of IPV identification and assistance programs. METHOD: A comprehensive literature search was conducted in the Medline, Embase, PyscInfo, and CENTRAL databases. The outcomes assessed in each included study were extracted and categorized, and the methodological quality of each eligible study was assessed using the Cochrane Risk of Bias tool. RESULTS: Of 20 eligible studies, 6 evaluated IPV identification programs and 14 studies examined IPV assistance programs. The included studies used 48 different outcomes that we classified into 10 categories. For identification studies, the most commonly used outcome categories were IPV disclosure (66.7%) and resource use (66.7%). The most commonly used outcome categories for the IPV assistance studies included IPV recurrence and severity (64.3%) and health outcomes (50%). The included studies demonstrated a number of methodological limitations as identified by the Cochrane Risk of Bias instrument. CONCLUSIONS: IPV identification and assistance programs are evaluated using many different outcome measures. Although this diversity enriches the IPV literature, it makes it challenging to compare studies. The results of this review highlight the challenges of conducting research in the field of IPV and the complexity of selecting, measuring, and interpreting 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 | 0.047 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.026 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".