Mentors in motion : a physical activity intervention for obese adolescents
Bibliographic record
Abstract
Childhood overweight and obesity has become a significant health concern worldwide. Obese youth are now being diagnosed with health complications and chronic diseases previously observed only among older adults. In order to improve their health and decrease their risk of premature mortality, secondary prevention is essential. To help guide development of an effective intervention program for obese youth referred to specialized clinical care, a chart review was conducted on adolescents seeking physician treatment for obesity. Data indicate that this patient population suffers from obesity-related health complications, faces social issues and exhibits lifestyle practices predisposing them to weight gain. Given that physical inactivity is one major risk factor for obesity in this population, a logic model and training module have been developed for a physical activity intervention program, with nutrition interventions to soon be incorporated. This program, called Mentors in Motion, provides mentoring to obese youth as a means of enabling positive changes in physical activity behaviors, mental well-being and overall health. A pilot study research protocol was also developed as part of the thesis activity to test the effectiveness of Mentors in Motion and to determine further program needs and enhancements. The pilot study has been funded by the Canadian Institutes for Health Research and is underway.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".