Thai women’s experiences of and responses to domestic violence
Bibliographic record
Abstract
Montakarn Chuemchit,1 Suttharuethai Chernkwanma,1 Ratana Somrongthong,1 Denise L Spitzer2 1College of Public Health Sciences, Chulalongkorn University, Patumwan, Bangkok, Thailand; 2Institute of Feminist and Gender Studies, University of Ottawa, Ottawa, ON, Canada Purpose: Domestic violence has been linked to many health consequences. It can impact women’s mental, physical, sexual, and reproductive health, and all of these effects can be long lasting. Despite the growing awareness of the deleterious effects of domestic violence in Thailand, there have been few nation-wide studies that have examined the issue and its consequences. In fact, Thailand has not examined intimate partner violence incidence for the past 20 years. This study aimed to investigate the consequences of domestic violence across the country. Subjects and methods: A cross-sectional study was conducted in four areas of Thailand: central, southern, northern, and northeastern. One province in each area was selected by simple random sampling techniques. One thousand four hundred and forty-four married or cohabiting females in a heterosexual union, aged 20–59 years, were included in the sample and were interviewed about their experiences of psychological, physical, and sexual violence by their male partners. Results: One thousand four hundred and forty-four women completed the interviews. Sixteen percent of respondents encountered domestic violence in its various psychological, physical, or sexual forms. In the majority of cases, all forms of domestic violence were exerted repeatedly. Four-fifths of women who faced domestic violence reported that it had an impact on their physical and mental health as well as employment. This study also found that half of the domestic violence survivors reported their children had witnessed violent situations. These women exercised four coping strategies to deal with their domestic violence: 1) counseling; 2) requesting help from others; 3) fighting back; and 4) running away from home. Conclusion: The findings confirm that domestic violence has implications that extend beyond health and result in the deterioration of the quality of women’s lives. These results underscore that domestic violence is a serious problem that must be addressed in Thai society. Keywords: domestic violence, intimate partner violence, Thailand, prevalence, consequences, violence countrywide, health impact, coping strategy
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".