Physical activity and health at the population level - the role of international networks
Bibliographic record
Abstract
The first countries to develop comprehensive policy approaches to population based physical activity promotion in the modern sense were Finland and Canada. Other parts of the worlds saw comparable developments in countries or large metropolitan areas towards the end of the 20th century, examples are Australia, Sao Paulo in Brazil and Japan. The US Surgeon General's report on Physical Activity and Health of 1996 was a health policy document with important international repercussions. Around the same time, the World Healt h Organisation (WHO) began to integrate physical activity in its Active Living Strategy and a little later in its Global Strategy for the Prevention and Control of Non-Communicable Diseases (NCDs). In 2004, physical activity featured more prominently in the WHO's Global Strategy on Diet, Physical Activity and Health. In the 2008-2013 Action Plan to WHO's NCD strategy, physical inactivity was for the first time explicitly and prominently named as one of the four main NCD risk factors. In 2010, WHO issued the first Global Recommendations for Physical Activity and Health, its role for global public health was confirmed in the 2011 UN High-Level Meeting on NCDs. So physical activity is a relatively new concept in international public health. Many institutions can play an important role in physical activity promotion, but for most this is only one of their tasks amongst others. There are a limited number of organisations that focus on physical activity and health alone: the international physical activity promotion networks and the International Society for Physical Activity and Health (ISPAH). Agita Mundo, the Global Physical Activity Promotion Network, the four existing regional networks (RAFA/PANA, HEPA Europe, APPAN and AFPAN), ISPAH and its advocacy council GAPA work together not only in the dissemination of tools and information, but also in lobbying for physical activity as an important aspect of public health. Physical activity and health - from the individual to the global level
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".