Orang Asli Health and Lifeways Project (OA HeLP): a cross-sectional cohort study protocol
Bibliographic record
Abstract
INTRODUCTION: Non-communicable disease (NCD) risk is influenced by environmental factors that are highly variable worldwide, yet prior research has focused mainly on high-income countries where most people are exposed to relatively homogeneous and static environments. Understanding the scope and complexity of environmental influences on NCD risk around the globe requires more data from people living in diverse and changing environments. Our project will investigate the prevalence and environmental causes of NCDs among the indigenous peoples of Peninsular Malaysia, known collectively as the Orang Asli, who are currently undergoing varying degrees of lifestyle and sociocultural changes that are predicted to increase vulnerability to NCDs, particularly metabolic disorders and musculoskeletal degenerative diseases. METHODS AND ANALYSIS: Biospecimen sampling and screening for a suite of NCDs (eg, cardiovascular disease, type II diabetes, osteoarthritis and osteoporosis), combined with detailed ethnographic work to assess key lifestyle and sociocultural variables (eg, diet, physical activity and wealth), will take place in Orang Asli communities spanning a gradient from remote, traditional villages to acculturated, market-integrated urban areas. Analyses will first test for relationships between environmental variables, NCD risk factors and NCD occurrence to investigate how environmental changes are affecting NCD susceptibility among the Orang Asli. Second, we will examine potential molecular and physiological mechanisms (eg, epigenetics and systemic inflammation) that mediate environmental effects on health. Third, we will identify intrinsic (eg, age and sex) and extrinsic (eg, early-life experiences) factors that predispose certain people to NCDs in the face of environmental change to better understand which Orang Asli are at greatest risk of NCDs. ETHICS AND DISSEMINATION: Approval was obtained from multiple ethical review boards including the Malaysian Ministry of Health. This study follows established principles for ethical biomedical research among vulnerable indigenous communities, including fostering collaboration, building cultural competency, enhancing transparency, supporting capacity building and disseminating research findings.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.015 |
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".