Analysis of Psychological Spouse Abuse against Men in Iranian Couples: A Qualitative Study
Bibliographic record
Abstract
This study aimed to explore the psychological spouse abuse against men in a sample of Iranian couples. This qualitative study used the grounded theory in triangulation. The statistical population consisted of a compilation of electronic resources, books, theses, and journals, family counseling experts’ opinions and men who suffered from spouse abuse in Isfahan. Purposeful sampling began and continued until saturation of the categories. Data collection consisted of 15 semi-structured interviews with abused married men, 10 interviews with family counseling experts and content analysis of books, articles, and journals related to psychological spouse abuse. The results showed that some cases of spouse abuse against the men are neglecting the spouse’ needs, lack of commitment and accountability, arbitrary behavior, turning to anti-moral values and some habits and annoying personality traits. Spouse abuse is rooted in social, religious, economic, environmental, cultural backgrounds and family life. Intervening factors include factors related to the others, personality traits of the abused person and background factors. The causal factors include the interpersonal conditions and personality types. The most important strategy for men against spouse abuse include aggressive-confronting response, silence, peaceful, defense, reform reaction and alternative reaction. The psychological consequences of marital violence include, individual, interpersonal, family, and social consequences. As a result, psychological spouse abuse against men is affected by a number of factors that were discussed in this study.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".