Why women suffer domestic violence in silence: Web-based responses to a blog
Bibliographic record
Abstract
Background & Aim: Domestic violence (DV) is a global socio-cultural concern faced by a majority of women. DV has a negative impact on women’s social, physical, and psychological wellbeing. Objective was to explore perceptions regarding contributing factors to domestic violence among women. Methods & Materials: A qualitative descriptive exploratory method was applied for the study. Purposive sampling was used to select participants through emails to respond to the web based blog created for the study. 41 worldwide participants shared their perceptions through the blogs in the study. The data were collected using a web-based discussion forum on the Urban Women Health Collaborative (UWHC), an internet-based social networking site, during March 2011. Data were analyzed, and categories and themes were extracted using a content analysis approach. Results: The major theme “Traditional values justifying domestic violence against women” emerged from the analysis of the participants’ blog. Under this major theme, four categories were extracted which include: socio-cultural attitudes towards women; trapped in the vicious cycle of violence; DV is a power game; and the misinterpretation of legal insinuations and religious practices. Conclusion: Women face DV due to social cultural practices and inequities in society. This implies that effective interventions are needed at several levels: individual, family, and community to prevent the violence and to provide a safe and respectful environment for the women in the society.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".