Association Between Birth Outcomes and Gestational Weight Gain Among Forcibly Displaced Rohingya and Nearby Host Community, in Cox’s Bazar, Bangladesh
Bibliographic record
Abstract
Gestational weight gain (GWG) is a critical determinant of maternal and neonatal health, yet its patterns and consequences in displaced populations remain understudied. This study examined the association between GWG and birth outcomes among Forcibly Displaced Rohingya (FDR) women in Cox’s Bazar, Bangladesh. We conducted a longitudinal cohort study from October 2022 to October 2024, enrolling 2888 pregnant women at different stages of pregnancy. Among them, 301 were recruited in the first trimester and followed through the third trimester, with 231 neonatal outcomes recorded within 72 hours of delivery. Overall, 66.8% of women experienced inadequate GWG. Despite the high prevalence of inadequate GWG, mean birth weight (2.79 kg) and mean gestational age at delivery (38.6 weeks) were within favorable ranges. Inadequate GWG was more common in mothers aged 30–39 years (p = 0.061) but significantly less common in underweight mothers (p = 0.012). GWG was positively associated with neonatal birth weight, length, and weight–length ratio (WLR) Z score, but not with gestational age. After adjusting for confounding factors, inadequate GWG showed a significant independent association with lower birth length (p = 0.016). These findings highlight the need for targeted interventions in displaced populations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".