Physical Activity Guidelines for the Brazilian Population: Development and Methods
Bibliographic record
Abstract
BACKGROUND: This article describes the process and methods used in the development of the first ever Physical Activity Guidelines for the Brazilian Population. METHODS: The steering committee established 8 working groups based on other guidelines and the Brazilian agenda for public health and physical activity (PA) promotion: (1) understanding PA; (2) children up to 5 years; (3) children and youth (6-17 y); (4) adults; (5) older adults (60 years and above); (6) physical education at school; (7) pregnant and postpartum women; and (8) people with disabilities. Working groups were formed to (1) synthesize the literature on each topic; (2) conduct workshops with stakeholders, health professionals, researchers, and the public; and (3) prepare a draft chapter for open online consultation. RESULTS: The document provides guidance for the population on the benefits of being active and recommendations regarding the amount (frequency, intensity, and duration) of PA recommended across all chapters. It also includes information on supporting networks for PA. CONCLUSIONS: The PA guidelines are widely accessible in Portuguese, including versions in English, Spanish, audiobook, and Braille, and will assist policy makers and professionals from several sectors to promote PA. The ultimate goal is to increase population levels of PA 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 | 0.053 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".