Women’s Participation in Household Decision Making and Justification of Wife Beating: A Secondary Data Analysis from Pakistan’s Demographic and Health Survey
Notice bibliographique
Résumé
Introduction: Globally, women’s empowerment is one of the important factors impacting the development of the nation. However, several women in developing countries, including Pakistan, experience a high level of gender discrimination and inequity. In this study, data from the Demographic and Health Survey (DHS) were used to measure empowerment and its predictors among women in Pakistan. Methods: Pakistan’s 2017–2018 DHS dataset was used to measure women’s empowerment using two indicators, i.e., participation in decision making and views on wife beating among 4216 married women. The determinants of empowerment, such as age, place of residence, regions, wealth index, education, partner’s education, partner’s occupation, number of children, consanguinity, the age difference between husband and wife, house and land ownership, and house inheritance, are reported as prevalence ratios (PRs) with a 95% confidence intervals (CI). Multivariate regression models were used to produce covariate-adjusted PRs and 95% CIs. Results: More than half of all women were empowered (52.5%). Upon multivariate analysis, we identified that women from the province of Punjab (adjusted PR (aPR), 1.44; 95% CI, 1.20–1.73), Sindh (aPR, 1.62; 95% CI, 1.35–1.96), and KPK (aPR, 1.09; 95% CI, 0.91–1.31) compared to those living in Baluchistan; from the richest quantile (aPR, 1.65; 95% CI, 1.37–1.99), followed by the richer quantile (aPR, 1.54; 95% CI, 1.28–1.84), the middle quantile (aPR, 1.52; 95% CI, 1.28–1.81), and the poorer quantile (aPR, 1.24; 95% CI, 1.04–1.47) compared to women who were from the poorest quantile; who were highly educated (aPR, 1.45; 95% CI, 1.25–1.67), followed by those who had a secondary education (aPR, 1.32; 95% CI, 1.16–1.50) and a primary education (aPR, 1.17; 95% CI, 1.02–1.35) compared to women who were not educated; and had exposure to mass media (aPR, 1.20; 95% CI, 1.06–1.36) compared to those who had no exposure were more empowered. Conclusion: To conclude, women’s empowerment in Pakistan is affected by various socioeconomic factors, as well as exposure to mass media. Targeted strategies are needed to improve access to education, employment, and poverty alleviation among women, particularly those living in rural areas. Various mass media advertisements should be practiced, targeting community norms and supporting women’s empowerment.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».