Intersectional inequalities in younger women’s experiences of physical intimate partner violence across communities in Bangladesh
Notice bibliographique
Résumé
BACKGROUND: Physical intimate partner violence (IPV) risk looms large for younger women in Bangladesh. We are, however, yet to know the association between their intersectional social locations and IPV across communities. Drawing on intersectionality theory's tenet that interacting systems of power, oppressions, and privileges work together, we hypothesized that (1) younger, lower educated or poor women's physical IPV experiences will be exacerbated in disadvantaged communities; and conversely, (2) younger, higher educated or nonpoor women's physical IPV experiences will be ameliorated in advantaged communities. METHODS: We applied intercategorical intersectionality analyses using multilevel logistic regression models in 15,421 currently married women across 911 communities from a national, cross-sectional survey in 2015. To test the hypotheses, women's probabilities of currently experiencing physical IPV among intersectional social groups were compared. These comparisons were made, at first, within each type of disadvantaged (e.g., younger or poor) and advantaged (e.g., older or nonpoor) communities; and then, between different types of communities. RESULTS: While our specific hypotheses were not supported, we found significant within community differences, suggesting that younger, lower educated or poor women were bearing the brunt of IPV in almost every community (probabilities ranged from 34.0-37.1%). Younger, poor compared to older, nonpoor women had significantly higher IPV probabilities (the minimum difference = 12.7, 95% CI, 2.8, 22.6) in all communities. Similar trend was observed between younger, lower educated compared to older, higher educated women in all except communities that were poor. Interestingly, younger women's advantage of higher education and material resources compared to their lower educated or poor counterparts was observed only in advantaged communities. However, these within community differences did not vary between disadvantaged and advantaged communities (difference-in-differences ranged from - 0.9%, (95% CI, - 8.5, 6.7) to - 8.6%, (95% CI, - 17.6, 0.5). CONCLUSIONS: Using intersectionality theory made visible the IPV precarity of younger, lower educated or poor women across communities. Future research might examine the structures and processes that put them at these precarious locations to ameliorate their socio-economic-educational inequalities and reduce IPV in all communities. For testing hypotheses using intersectionality theory, this study might advance scholarship on physical IPV in Bangladesh and quantitative intersectionality globally.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».