How Valid is the Question of Fear of a Partner in Identifying Intimate Partner Abuse? A Cross-Sectional Analysis of Four Studies
Bibliographic record
Abstract
Intimate partner abuse (IPA) affects women's health, requiring accurate questions to identify the abuse. We investigated the accuracy of three questions about fear of an intimate partner in identifying exposure to IPA. We compared the sensitivity and specificity of these questions with the Composite Abuse Scale (CAS) using secondary data analysis of four existing studies. All studies recruited adult women from clinical settings, with sample sizes ranging from 1,257 to 5,871. We examined associations between demographic factors and fear through multivariate logistic regression, and analysis of the sensitivity and specificity of the questions about fear and IPA (CAS), generating a receiver operating curve (ROC). The prevalence of lifetime fear of a partner ranged from 9.5% to 26.7%; 14.0% of women reported fear in the past 12 months; and current fear ranged from 1.3% to 3.3%. Comparing the three questions, the question "afraid of a partner in the past 12 months" was considered the best question to identify IPA. This question had the greatest area under the ROC (0.80, 95% confidence interval (CI) = [0.78-0.81]) compared with "are you currently afraid" (range 0.57-0.61) or "have you ever been afraid" (range 0.66-0.77); and demonstrated better sensitivity (64.8%) and specificity (94.8%). Demographic factors associated with "fear of a partner in the past 12 months" included being divorced/separated (odds ratio [OR] = 8.49, 95% CI = [6.70-10.76]); having a low income (OR = 4.21, 95% CI = [3.46-5.13]); and having less than 12 years of education (OR = 2.48, 95% CI = [2.04-3.02]). The question "In the last 12 months did you ever feel frightened by what your partner says or does?" has potential to identify a majority of women experiencing IPA, supporting its utilization where more comprehensive measures are not possible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".