Does child gender determine household decision for health care in rural Thatta, Pakistan?
Bibliographic record
Abstract
BACKGROUND: In South Asia, gender disparity in child mortality is highest in Pakistan. We examined the influence of child gender on household decision regarding health care. METHODS: Prevalence ratios were calculated for 3740 children aged 1-59 months from 92 randomly selected villages of rural Pakistan using a cluster-adjusted log-binomial model. Level 1 variables included child and household characteristics and level 2 included village characteristics. RESULTS: There were 25 more girl deaths than boys per 1000 live births (95% CI: 13.9, 48.6) among post-neonates and 38 more among children aged 12-59 months (95% CI: 10.5, 65.5). However, in adjusted analysis, gender was not a significant predictor of illness reporting, visit to health facilities, choice of provider, hospitalization and health expenditure. Significant predictors of health care were child's age, illness characteristics, number of children in the family, household socio-economic status and absence of girls' school in the village. CONCLUSIONS: Differential care seeking for boys and girls is not seen in Thatta despite clear differences in mortality ratios. This calls for more creative research to identify pathways for gender differential in child mortality. Factors identified as influencing child health care and amenable to modification include poverty alleviation and girls' education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".