Are pre-school girls more likely to be under-nourished in rural Thatta, Pakistan?-a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Pakistan ranks third lowest on a global gender index (2013) and 13(th) highest on the prevalence of underweight among under-five children (2010). Through this population-based study, we examined gender differentials in the prevalence of stunting, wasting and under-weight defined by World Health Organization (WHO) Growth Standard among rural pre-school Pakistani children. METHODS: We performed secondary analysis of data collected through a cross-sectional survey of Thatta district during 1992-93. Prevalence ratios were calculated for 1051 children aged 0-35 months from 95 randomly selected villages of rural Pakistan using a clustered adjusted log binomial model. Level 1 variables included child and household characteristics and level 2 included village characteristics. RESULTS: Based on the new WHO growth reference, a major proportion of children were stunted (52.9 %), wasted (22.9 %) and under-weight (46.5 %). In a two-level model, compared to boys, girls had significantly greater risk of stunting [Prevalence Ratio (PR) (95 % C.I.) = 1.18 (1.03, 1.36)] and under-weight [P.R. (95 % C.I.) 1.14 (1.03, 1.26)], after adjustment of maternal literacy and village variables. Risk of wasting did not differ with gender [P.R. (95 % C.I.) = 1.04 (0.99, 1.15)]. Mothers of stunted and underweight children were respectively, 21 and 20 % more likely to be illiterate than those of normally nourished children. Sick children were at 16 % greater risk of wasting than those not reported ill. CONCLUSION: Greater prevalence of stunting and under-weight among girls suggests adoption of a gender sensitive approach in nutritional intervention programmes. Prompt management of childhood illnesses may reduce prevalence of wasting. Better literacy among rural mothers may reduce prevalence of stunting and under-weight. Whether gender differences in nutrition status are an underlying pathway for excessive girl mortality in rural Thatta needs further examination.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".