Factors Influencing Post-Marriage Education and Employment among Bangladeshi Women: A Cross-Sectional Analysis
Bibliographic record
Abstract
Higher education and employment are two key components of women’s empowerment. However, many women fail to continue their studies or work after marriage, which can significantly reduce their empowerment potential, especially in countries with stark gender inequality such as in Bangladesh. In this study, our objective was to explore the individual, household and community factors associated with post-marriage education and employment among Bangladeshi women using data from the latest Bangladesh Demographic and Health Survey (BDHS 2017–18). Data were analysed using multivariate logistic regression methods. The results of the study show that a large proportion of the participants did not continue their studies (42.1%) or work (72.5%) after marriage, while only 3% of the participants studied and about 29.0% worked for more than 5 years after marriage. The most important factors associated with continuing to study after marriage include having access to a mobile phone (OR = 1.89, 95% CI = 1.62, 2.19), the husband’s number of years of education (OR = 1.11, 95% CI = 1.08, 1.15), a higher household wealth index (OR = 1.27–4.31) and improved toilet facilities (OR = 1.36, 1.12, 1.65). Conversely, the number of children (OR = 0.69, 95% CI = 0.65, 0.73), living in rural areas (OR = 0.78, 95% CI = 0.68, 0.88) and residing in certain divisions are negatively associated with continuing to study after marriage. Women with a mobile phone (OR = 1.47, 95% CI = 1.06, 2.03) are more likely to continue working after marriage, while those with larger spousal age differences (OR = 0.33, 95% CI = 0.19, 0.58) and those living in the Chittagong division (OR = 0.53, 95% CI = 0.30, 0.96) are less likely to do so. The study indicates that a large proportion of Bangladeshi women do not continue their education or work after marriage. These findings underscore the significance of empowering women and addressing sociodemographic issues to promote education and work opportunities after marriage.
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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 | 0.001 |
| 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 | 0.000 | 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".