Physical activity promotion in primary care: a Utopian quest?
Bibliographic record
Abstract
The health benefits of physical activity (PA) are acknowledged and promoted by the scientific community, especially within primary care. However, there is little evidence that such promotion is provided in any consistent or comprehensive format. Brief interventions (i.e. discussion, negotiation or encouragement) and exercise referral schemes (i.e. patients being formally referred to a PA professional) are the two dominant approaches within primary care. These cost-effective interventions can generate positive changes in health outcomes and PA levels in inactive patients who are at increased risk for non-communicable diseases. Their success relies on the acceptability and efficiency of primary care professionals to deliver PA counselling. To this end, appropriate training and financial support are crucial. Similarly, human resourcing and synergy between the different stakeholders must be addressed. To obtain maximum adherence, specific populations should be targeted and interventions adapted to their needs. Key enablers include motivational interviewing, social support and multi-disciplinary approaches. Leadership and lines of accountability must be clearly delineated to ensure the success of the initiatives promoting PA in primary care. The synergic and multisectoral action of several stakeholders, especially healthcare professionals, will help overcome physical inactivity in a sustainable way.
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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 | 0.035 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".