Cohort profile: A prospective cohort study on newlywed couples in rural and poor urban Bangladesh
Bibliographic record
Abstract
Sexual and Reproductive Health and Rights (SRHR) aim to enhance quality of life through safe sexual experiences, reproductive autonomy, and protection against gender-based violence. However, existing SRHR research and interventions in low- and middle-income countries like Bangladesh predominantly focus on women, often understating men and neglecting the nuanced contextual issues faced by married couples. This study contributes to filling this gap by examining SRHR dynamics among newlyweds in rural and poor urban areas of Bangladesh, especially focusing on marital satisfaction, fertility preferences, and post-marriage adaptation mechanisms. Employing a prospective cohort design across four Health and Demographic Surveillance Systems (HDSS) managed by icddr,b, the study spans from November 2021 to March 2025, with data collection starting in December 2022. Of the 2011 newlywed couples identified, 666 who met eligibility criteria (married for ≤6 months, first marriage, and no pregnancy history) were enrolled. Participants will undergo six quantitative interview sessions over a two-year period. Additionally, 44 in-depth qualitative interviews were conducted with 22 purposefully selected couples. Demographic data reveal that a significant proportion of husbands (67.3% in rural areas, 71.8% in poor urban areas) are aged 20-29 years, while a majority of wives (67.9% in rural areas, 84.8% in poor urban areas) are adolescents. Education levels varied, with a higher proportion of poor urban husbands lack formal education compared to their rural counterparts (7.2% vs. 3.0%), while no significant variation was observed among wives (0.6% vs 1.0%). Arranged marriages are more common among rural couples (80%) compared to those in poor urban areas (50%). Moreover, poor urban participants tend to marry at a younger age than the rural participants, with poor urban wives marrying earlier than rural wives (60.4% vs 39.7%). This pioneering study provides valuable insights into the SRHR needs of newlywed couples in Bangladesh. The findings will be instrumental for designing targeted interventions aimed at improving SRHR service utilization and enhancing overall well-being, particularly in rural and poor urban areas of the country.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".