Regional variations of contraceptive use in Bangladesh: A disaggregate analysis by place of residence
Bibliographic record
Abstract
This study advances current knowledge on contraceptive use in Bangladesh by providing new insights into the extent of regional variations in contraceptive use across rural and urban areas of Bangladesh. We examined the regional variations in contraceptive use among 15,699 currently married women ages 15-49 years using data from the 2014 Bangladesh Demographic and Health Survey (BDHS). Multivariate logistic regression models of contraceptive use were calibrated with sociodemographic attributes and cultural factors. Based on the aggregate sample (i.e., rural and urban combined), we found significant regional variations in contraceptive use across the administrative divisions in Bangladesh. Based on a disaggregate sample (i.e., rural and urban separately), we found that there were significant differences in divisional variations in contraceptive use in rural areas. In contrast, no significant variation in contraceptive use across divisions in urban areas of Bangladesh was found. More specifically, among women living in rural areas, the Rajshahi and Rangpur divisions had higher odds of contraceptive use than the Barisal division, whereas the Chittagong and Sylhet divisions had much lower odds of contraceptive use even after adjusting for selected sociodemographic attributes and cultural factors. A separate analysis of the divisional variations in usage of modern methods of contraception also revealed similar findings with only one exception. Findings of this study provide an evidence-based direction for adapting a pragmatic approach to reducing the divisional disparity of contraceptive use in rural areas of Bangladesh.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".