Social sector drivers and stunting reduction in Pakistan: A subnational analysis
Bibliographic record
Abstract
BACKGROUND: Poor nutrition remains a problem in Pakistan, and despite economic growth, stunting reduction is slow with subnational variations. Generally, stunting prevalence is lower in Khyber Pakhtunkhwa (KP) and Northern Punjab than in Sindh and Balochistan. OBJECTIVES: Our study aimed to identify and explore key drivers and barriers underlying subnational changes in stunting between 2011 and 2018. METHODS: We used a mixed-methods approach to determine the drivers of and barriers to change in stunting [height-for-age z-score (HAZ) < -2], including Oaxaca-Blinder decomposition and multivariable hierarchical modeling, using representative household nutrition surveys in 2011 and 2018. These findings were triangulated with a literature review, program and policy analysis, and primary qualitative research to generate a narrative of change. RESULTS: National decline in stunting prevalence was limited (2011: 42.3%, 2018: 40.2%); however, progress varied between provinces with significant stunting declines in KP and Northern and Southern Punjab [0.49 standard deviation (SD) gain in HAZ between 2011 and 2018 (43.0% to 38.3%)]. Factors driving HAZ increases were improvements in socioeconomic status (0.13 SD), coverage of Pakistan's income support program targeting females (0.05 SD), and a combination of other direct and indirect health and nutrition investments (0.1 SD). Our policy analysis and qualitative findings also supported these findings by identifying poverty and increased food prices as a barrier to accessing nutritious foods. CONCLUSIONS: Although national progress in stunting reduction remains slow, regions of KP, Northern, and Southern Punjab have demonstrated significant reductions in child stunting prevalence between 2011 and 2018. Our analysis supports continued investments in social sector programs targeting females' health and status and strengthening the reach and quality of existing maternal and child health and nutrition programs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".