Drivers of malnutrition among late adolescent and young women in rural Pakistan: a cross-sectional assessment of the MaPPS trial
Bibliographic record
Abstract
OBJECTIVE: This study aimed to characterise the burden of malnutrition and assess how underlying determinants at the structural and intermediary levels contributed to malnutrition among late adolescent and young women in rural Pakistan. DESIGN: Cross-sectional enrolment data assessment. SETTING AND PARTICIPANTS: This study was conducted using data from adolescent and young women (n=25 447) enrolled in the Matiari emPowerment and Preconception Supplementation Trial, collected from June 2017 to July 2018 in Matiari District, Pakistan. The WHO-based cut-offs were applied to anthropometric measures to estimate body mass index (BMI) categories (underweight, overweight, obese) and stunting. Hierarchical models were generated to evaluate the association between the determinants with BMI categories and stunting among late adolescent girls and young women, respectively. PRIMARY AND SECONDARY OUTCOME MEASURES: The main outcomes of interest were BMI categories and stunting. Explanatory variables included measures of socioeconomic status, education, occupation, health, well-being, food security, empowerment and food practices. RESULTS: Regardless of age group, the prevalence of underweight was high (36.9%; 95% CI 36.3% to 37.5%). More late adolescent girls were underweight, while more young women were overweight/obese (p<0.001). Stunting affected 9.2% (95% CI 8.9% to 9.6%) of participants, of which 35.7% were additionally underweight and 7.3% overweight/obese. Compared with those in the normal weight category, those underweight were more likely to be impoverished and less empowered. Those overweight/obese were more likely to be from a higher wealth quintile and food secure. Increased education level and food security were associated with reductions stunting risk. CONCLUSIONS: This study informs the data gap and need for comprehensive research on adolescent nutritional status. Findings suggest factors related to poverty played an important, underlying role in undernutrition among participants. Commitment to improving the nutritional status of all adolescent and young women in Pakistan will be critical given the observed burden of malnutrition. TRIAL REGISTRATION NUMBER: NCT03287882.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".