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Record W2603901197 · doi:10.3138/jcfs.43.5.647

Intimate Partner Violence and Domestic Violence Myths: A Comparison of Women With and Without Alcoholic Husbands (A Study from India)

2012· article· en· W2603901197 on OpenAlexvenueno aff
Selwyn Stanley

Bibliographic record

VenueJournal of Comparative Family Studies · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsnot available
Fundersnot available
KeywordsDomestic violenceBlamePsychologyConflict tactics scaleSocial psychologyPsychological interventionPoison controlAggressionClinical psychologyInjury preventionPsychiatryMedicineMedical emergency

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.836

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.089
GPT teacher head0.428
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2012
Admission routes1
Has abstractyes

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