Nash-wo-Numa (childhood growth & development) study protocol: Factors that impact linear growth in children 9 to 15 years of age in Matiari, Pakistan
Bibliographic record
Abstract
Introduction: Adolescence is a time of significant physical and emotional change, and there is emerging concern that adolescents living in low- and middle-income countries (LMIC) may face substantial challenges in relation to linear growth and mental health. Data on the global burden of stunting after 5 years of age are limited, but estimates suggest up to 50 per cent of all adolescents in some LMIC are stunted. Additionally, many LMIC lack robust mental health care delivery systems. Pakistan has one of the world's largest populations of adolescents (10 to 19 years) at approximately 40 million. The Nash-wo-Numa study's primary objective is to assess the prevalence and risk factors for stunting among early adolescents in rural Pakistan. The study also aims to determine the prevalence of poor mental health and identify factors associated with common mental health concerns during the childhood to adulthood transition.Methods: This cross-sectional study will include girls (n= 738) 9.0 to 14.9 years of age and boys (n=687) 10.0 to 15.9 years of age who live in the rural district of Matiari, Pakistan. Participants will be assessed for anthropometrical measures, puberty development, nutritional biomarkers as well as symptoms of depression, anxiety and trauma using validated scales.Ethics and Dissemination: The proposed study aims to complete the picture of child and adolescent health concerning linear growth and mental health by including puberty indicators. Ethics approval has been granted by the Ethics Review Committee at the Aga Khan University, Karachi, Pakistan, #5251-WCH-ERC-18 and Research Ethics Board at SickKids Hospital, Toronto, Canada, #:1000060684. Study results will be presented at relevant conferences and published in peer-reviewed journals.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.009 |
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".