Evidence-Based Medicine: Acknowledging the Role for Physical Activity
Bibliographic record
Abstract
Modern technology and lifestyles have created an environment that predisposes our population to inactivity, resulting in fewer people meeting the Canadian Physical Activity Guidelines. There is a clear link between inactivity and the risk of developing chronic health conditions including hypertension, type 2 diabetes, and cancer; however, exercise prescription and counselling by physicians is lacking. This may in part be attributed to inadequate training of physicians during medical school. In this commentary, we outline the demand for awareness and training of physicians to prepare them to prescribe physical activity, and propose steps to increase exercise prescription for improved population health. La technologie moderne ainsi que nos habitudes de vie actuelles nous prédisposent à l’inactivité ce qui mène moins de personnes à respecter les directives canadiennes en matière d’activité physique. Un lien direct existe entre l’inactivité et le risque de développer des problèmes de santé chroniques incluant l’hypertension, le diabète de type 2, et le cancer. Toutefois, l’exercice et le counseling prescrits par les médecins sont peu pratiqués par les patients qui pourraient en bénéficier. Dans cet article, nous soulignerons le besoin de formation des médecins afin de mieux les préparer à prescrire de l’activité physique à leur patients et leur proposer des étapes pour améliorer la santé physique de la population.
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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 | 0.040 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.016 | 0.027 |
| Insufficient payload (model declined to judge) | 0.005 | 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".