Intimate Partner Violence and Domestic Violence Myths: A Comparison of Women With and Without Alcoholic Husbands (A Study from India)
Bibliographic record
Abstract
The alcoholism literature is replete with evidence documenting its adverse consequences for the family system in general and the interpersonal relationships of spouses in particular. An ex-post facto crossectional design was used to compare 150 women from India having alcoholic husbands with an equal number of women without alcoholic spouses. The two groups matched on key socio-demographic variables, were administered the Domestic Violence Myths Acceptance Scale (DVMAS, Peters, 2008) and the Revised Conflict Tactics Scale (CTS, Straus et al., 1996), to examine myths relating to domestic violence and the conflict tactics used by them. Higher levels of conflict were seen in the wives of alcoholics and on all its sub-dimensions namely negotiation, physical assault, injury, psychological aggression and sexual coercion. Differences were also significant on the subdimensions of the DVMAS namely character blame, behavior blame, perpetrator exoneration and minimization. Analysis of variance showed that wives of alcoholics from nonconsanguineous or arranged marriages or nuclear families did not differ significantly from their counterparts in the reference group on the subject dimensions studied. Results indicate the need to address issues relating to conflict and domestic violence related myths as part of therapeutic interventions with wives of alcoholics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".