Intersectional community correlates of married women’s experiences of male intimate partner physical violence in Bangladesh: a cross-sectional study
Bibliographic record
Abstract
Background In Bangladesh, little is known about community-level factors shaping married women’s experiences of male intimate partner physical violence (MIPPV); it is also unknown if these factors interact with each other. We examined the (1) association between four residential community characteristics defined by the attributes of ever married women in those communities–younger age, lower education, higher participation in earning an income and poverty; and (2) two-way interactions between these community-level MIPPV correlates. Methods We used a cross-sectional sample comprising 14 557 currently married women who were living with their spouses from 911 Bangladeshi communities. Data were collected during 13–22 August 2015. Conflict Tactics Scale-2 measured the outcome–women’s current MIPPV experiences; and multilevel logistic regression models predicted this outcome. Results Four community characteristics including higher proportions of women’s earning an income and achieving higher education were not associated with their increased likelihood of experiencing MIPPV. However, women living inhigher earning participation, higher educatedcommunities were significantly more likely to experience MIPPV than those inlower earning participation, higher educatedcommunities (predicted probability, p=0.30, 95% CI 0.26 to 0.34 vs p=0.24, 95% CI 0.22 to 0.25). Conclusion This is the first study to examine interactions between women’s community-level MIPPV correlates in Bangladesh. Although we did not find support for the relationship between women’s most intersectional community-level locations and MIPPV, we did find a currently invisible vulnerable intersectional location:higher earning participation, higher educatedcommunities. Bangladeshi violence against women prevention policies and programmes, therefore, need to engage with these particular communities to tackle head on male responses to these locations to reduce MIPPV.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".