A Comparison of the Types of Screening Tool Administration Methods Used for the Detection of Intimate Partner Violence
Bibliographic record
Abstract
Intimate partner violence (IPV) is associated with significant health consequences for victims, including acute/chronic pain, depression, trauma, suicide, death, as well as physical, emotional, and mental harms for families and children. The objective of this systematic review and meta-analysis was to assess the rate of IPV disclosure in adult women (>18 years of age) with the use of three different screening tool administration methods: computer-assisted self-administered screen, self-administered written screen, and face-to-face interview screen. A comprehensive literature search was conducted in the MEDLINE, EMBASE, PsycINFO, CINAHL, Database of Abstracts of Reviews of Effectiveness, and the Cochrane library databases. We identified 746 potentially relevant articles; however, only 6 were randomized controlled trials (RCTs) and included for analysis. No significant differences were observed when women were screened in face-to-face interviews or with a self-administered written screen (Odds of disclosing: 1.02, 95% confidence interval [CI]: [0.77, 1.35]); however, a computer-assisted self-administered screen was found to increase the odds of IPV disclosure by 37% in comparison to a face-to-face interview screen (odds ratio: 0.63, 95% CI: [0.31, 1.30]). Disclosure of IPV was also 23% higher for computer-assisted self-administered screen in comparison to self-administered written screen (Odds of disclosure: 1.23, 95% CI: [0.0.92, 1.64]). The results of this review suggest that computer-assisted self-administered screens leads to higher rates of IPV disclosure in comparison to both face-to-face interview and self-administered written screens.
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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 | 0.029 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.029 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".