Implementing national population based action on physical activity- for action and opportunities for international collaboration
Bibliographic record
Abstract
This paper summarises recent past and current international developments on physical activity looking at the challenges and opportunities they pose. Key elements of the WHO's Global Strategy on Diet, Physical Activity and Health (GSDPAH) are summarised, focusing specifically on the physical activity components, and by drawing upon recent fora (Atlanta, October 2002; Miami, December 2004; Cascais, February 2005; Beijing, October 2005; Bogotá, November 2005), we outline the barriers and areas of support required for successful development and implementation of national, population-based action on physical activity. These gatherings focused particularly on the needs of developing countries, where to date little has been done to augment physical activity at a population level. Unless swift action is taken, these countries will soon suffer significantly from an increased prevalence of non communicable diseases (NCD). Existing initiatives and opportunities for national and international action on physical activity are identified. Specific actions are proposed for advocacy, communication and dissemination, networks and partnerships, fundraising, policy development and implementation, programme implementation and evaluation, surveillance and capacity building. The development of the Global Alliance for Physical Activity (GAPA) provides a structure for international collaboration.
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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 | 0.063 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".