Birth registration coverage according to the sex of the head of household: an analysis of national surveys from 93 low- and middle-income countries
Bibliographic record
Abstract
BACKGROUND: Within-country inequalities in birth registration coverage (BRC) have been documented according to wealth, place of residence and other household characteristics. We investigated whether sex of the head of household was associated with BRC. METHODS: Using data from nationally-representative surveys (Demographic and Health Survey or Multiple Indicator Cluster Survey) from 93 low and middle-income countries (LMICs) carried out in 2010 or later, we developed a typology including three main types of households: male-headed (MHH) and female-led with or without an adult male resident. Using Poisson regression, we compared BRC for children aged less than 12 months living the three types of households within each country, and then pooled results for all countries. Analyses were also adjusted for household wealth quintiles, maternal education and urban-rural residence. RESULTS: BRC ranged from 2.2% Ethiopia to 100% in Thailand (median 79%) while the proportion of MHH ranged from 52.1% in Ukraine to 98.3% in Afghanistan (median 72.9%). In most countries the proportion of poor families was highest in FHH (no male) and lowest in FHH (any male), with MHH occupying an intermediate position. Of the 93 countries, in the adjusted analyses, FHH (no male) had significantly higher BRC than MHH in 13 countries, while in eight countries the opposite trend was observed. The pooled analyses showed t BRC ratios of 1.01 (95% CI: 1.00; 1.01) for FHH (any male) relative to MHH, and also 1.01 (95% CI: 1.00; 1.01) for FHH (no male) relative to MHH. These analyses also showed a high degree of heterogeneity among countries. CONCLUSION: Sex of the head of household was not consistently associated with BRC in the pooled analyses but noteworthy differences in different directions were found in specific countries. Formal and informal benefits to FHH (no male), as well as women's ability to allocate household resources to their children in FHH, may explain why this vulnerable group has managed to offset a potential disadvantage to their children.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".