Association between short birth spacing and child malnutrition in Bangladesh: a propensity score matching approach
Bibliographic record
Abstract
OBJECTIVES: This study aimed to explore the effects of short birth spacing (SBS), which is defined as a period of less than 33 months between two successive births, on multiple concurrent forms of child malnutrition (MCFCM) and at least one form of child malnutrition (ALOFCM) using propensity score matching (PSM). METHODS: This study used data extracted from the 2017-18 Bangladesh Demographic and Health Survey. PSM with four different distance functions, including logistic regression, classification and regression tree, single hidden layer neural network and random forest, were performed to evaluate the effects of SBS on MCFCM and ALOFCM. We also explored how the effects were modified in different subsamples, including women's empowerment, education and economic status (women's 3E index)-constructed based on women's decision-making autonomy, education level, and wealth index, and age at marriage, and place of residence. RESULTS: The prevalence of SBS was 22.16% among the 4652 complete cases. The matched samples of size 2062 generated by PSM showed higher odds of MCFCM (adjusted OR (AOR)=1.25, 95% CI=1.02 to 1.56, p=0.038) and ALOFCM (AOR=1.20, 95% CI=1.01 to 1.42, p=0.045) for the SBS children compared with their counterparts. In the subsample of women with 3E index≥50% coverage, the SBS children showed higher odds of MCFCM (AOR: 1.43, 95% CI=1.03 to 2.00, p=0.041] and ALOFCM (AOR: 1.33, 95% CI=1.02 to 1.74, p=0.036). Higher odds of MCFCM (AOR=1.27, 95% CI=1.02 to 1.58, p=0.036) and ALOFCM (AOR=1.23, 95% CI=1.02 to 1.51, p=0.032) for SBS children than normal children were also evident for the subsample of mothers married at age≤18 years. CONCLUSION: SBS was significantly associated with child malnutrition, and the effect was modified by factors such as women's autonomy and age at 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".