Identifying actionable lifestyle risk factors for obesity research and intervention: Challenges and opportunities for Pacific Island health researchers
Bibliographic record
Abstract
In The Lancet Regional Health – Western Pacific, Frayon and colleagues assess sociodemographic and lifestyle risk factors for overweight and obesity among adolescents in New Caledonia [1]. The prevalence of adolescent overweight and obesity is high and growing across Pacific Island Countries and Territories (PICTs) [2]. Wide variation in risk among countries and among ethnic groups, such as elevated risk among Melanesian and Polynesian adolescents in New Caledonia, points to the importance of studies assessing diverse samples that could highlight shared risk factors to inform population-wide interventions, and patterns specific to each group that might guide more tailored interventions and clinical practice.
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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 | 0.233 | 0.251 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.014 | 0.044 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".