Psychometric Validation of Culturally Adapted Tool for Measuring Attitudes Toward Domestic Violence: Bridging Social Norms and Community Interventions
Bibliographic record
Abstract
INTRODUCTION: Domestic violence against women (DVAW) is a global health issue, particularly in areas with patriarchal norms and a lack of culturally relevant assessment tools. We developed a culturally adapted questionnaire to measure community attitudes toward DVAW in Saudi Arabia. METHODS: We conducted a cross-sectional study involving expert review, exploratory factor analysis (EFA), and assessments of internal consistency. The study included a convenience sample of 831 adult participants, comprising 417 males and 414 females. RESULTS: The adapted instrument showed excellent psychometric properties (Cronbach's alpha = 0.93) and strong validity across demographic groups. Although excluding acceptance-oriented items narrowed the conceptual scope, the remaining items effectively measure key dimensions of DVAW rejection, reflecting community attitudes and their mental health implications. DISCUSSION: This reliable instrument measures community attitudes toward DVAW. Future work may explore cognitive neuroscience methods to study attitude change and the long-term effects of culturally adapted interventions on mental health.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".