Determinants associated with high-risk fertility behaviours among reproductive aged women in Bangladesh: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: We aimed to determine the factors that increase the risk of HRFB in Bangladeshi women of reproductive age 15-49 years. METHODS: The study utilised the latest Bangladesh Demographic and Health Survey (BDHS) 2017-18 dataset. The Pearson's chi-square test was performed to determine the relationships between the outcome and the independent variables, while multivariate logistic regression analysis was used to identify the potential determinants associated with HRFB. RESULTS: Overall 67.7% women had HRFB among them 45.6% were at single risk and 22.1% were at multiple high-risks. Women's age (35-49 years: AOR = 6.42 95% CI 3.95-10.42), who were Muslims(AOR = 5.52, 95% CI 2.25-13.52), having normal childbirth (AOR = 1.47, 95% CI 1.22-1.69), having unwanted pregnancy (AOR = 10.79, 95% CI 5.67-18.64) and not using any contraceptive methods (AOR = 1.37, 95% CI 1.24-1.81) were significantly associated with increasing risk of having HRFB. Alternatively, women and their partners' higher education were associated with reducing HRFB. CONCLUSION: A significant proportion of Bangladeshi women had high-risk fertility behaviour which is quite alarming. Therefore, the public health policy makers in Bangladesh should emphasis on this issue and design appropriate interventions to reduce the maternal HRFB.
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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 | 0.000 | 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) | 0.002 | 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".