Effective Non-clinical Interventions \nto Prevent and Treat Childhood Overweight and Obesity in \nNewfoundland and Labrador
Bibliographic record
Abstract
Newfoundland & Labrador (NL) has the \nhighest prevalence of overweight and \nobesity for adults and children among the \nprovinces of Canada. \nSince the 1980’s, childhood overweight \nhas increased by 28% and obesity by 175%. \nNearly 80% of middle-school and highschool \nchildren in the province \ndo not get enough exercise, \nand it is estimated that only a \nquarter of children and adolescents \nare physically active at all \nin their spare time. \nThe causes of the current \ngrowth in childhood obesity \nare complex and interrelated. NL exhibits \nall the major clusters of risk factors for \nchildhood overweight and obesity. \nOverweight and obesity have significant \nhealth consequences involving increases \nin the risk of weight-related diseases. \nThese diseases accounted for $1.6B in \ndirect costs to the Canadian health care \nsystem, and $4.3B in indirect costs to \nthe country as a whole. From 1985 to 2000, it is estimated that the number of \ndeaths in NL related to overweight and \nobesity increased 58.9%. Since a child \nwho becomes overweight or obese is less \nlikely to return to a healthy weight status, \nchildhood obesity presents significant \nlong-term health and economic challenges \nto the province. \nA wide and diverse range of prevention \nand treatment interventions \nhas been developed to address \nchildhood overweight and obesity. \nThe purpose of this synthesis is to \nsummarize the research findings on \nthe prevention and treatment of \nchildhood obesity, with the goal of \ninforming policy and program design. \nThe review is not intended to recommend \nany specific intervention over any \nother, or to evaluate existing programs. \nThe findings of this synthesis are intended \nto provide information that can be used \nin making decisions about how to develop, \nimplement and modify childhood obesity \ninterventions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".