Women’s Participation in Household Decision Making and Justification of Wife Beating: A Secondary Data Analysis from Pakistan’s Demographic and Health Survey
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".