Development of the Brazilian Version of a Pan-Canadian Behavior Change Program and Its Health and Fitness Outcomes
Bibliographic record
Abstract
Chronic diseases are a major health problem worldwide, especially in lower-income jurisdictions. Considering this scenario, the World Health Organization has recently established, as a research priority, preventive interventions for populations from lower-income countries, such as the middle-income country of Brazil. The purpose of this article is to describe the components of a pan-Canadian lifestyle program adapted to Brazilians and to report its health and fitness outcomes. A 12-week program called ACCELERATION was translated and culturally adapted to Brazilians. A quasi-randomized controlled trial was designed, consisting of weekly emails and educational videos addressing risk factors for chronic disease. Health and fitness measures included body composition, cardiovascular variables, aerobic fitness, and muscular strength. The Brazilian experimental group showed maintenance in heart rate, blood pressure, and VO2max values while presenting an improvement of 3.3% in body fat percentage (p = 0.040, d = −0.325) and 5.1% in muscular strength (p = 0.039, d = 0.328). Overall, these results were similar to the Canadian intervention. Based on these findings, the Brazilian version of the program has the potential to contribute to the fight against chronic diseases in Brazil.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".