Bibliographic record
Abstract
In 2011, Leduc Beaumont Devon Primary Care Network (PCN) began a Prescription to Get Active Initiative. The Leduc Beaumont Devon area is a mixed suburban and rural population just outside of Edmonton, Alberta. Prescription to Get Active is an integrated partnership between primary care, Alberta Health Services, municipal and private recreation facilities to promote the importance of daily regular physical activity. In early 2014, this initiative expanded to the greater Edmonton area. Prescription to Get Active targets low risk, sedentary individuals who are not meeting Canada's physical activity guidelines by addressing common barriers of motivation and access. Participating physicians and other healthcare professionals provide a written prescription for physical activity which can be turned in to receive complimentary access to participating facilities. We have asked our partner facilities to report the number of prescriptions redeemed between the regional launch in February 2014 to March 2015. With approximately 1000 family physicians and primary care allied providers participating in the initiative, 682 prescriptions have been redeemed across the Edmonton zone to date. Partnering facilities report that between 20-40% of patients attending with a Prescription to Get Active continue past the initial complementary access period to purchase a longer term membership. Our results indicate that prescribing physical activity by a family physician or a member of their team can lead to successful behavior change. This program supports the premise that a strong partnership between primary care and community recreation is a critical component of a physical activity prescription program.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.004 |
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".