The ‘miracle cure’: how do primary care physicians prescribe physical activity with the aim of improving clinical outcomes of chronic disease? A scoping review
Bibliographic record
Abstract
Objectives: To identify how primary care physicians (PCPs) prescribe physical activity for patients with chronic disease, and to determine characteristics of physical activity interventions with improved clinical outcomes of chronic disease. Design: A scoping review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews 2018 was completed. Data sources: Four bibliographic databases (Medline, EMBASE, SPORTDiscus, CINAHL) and four grey literature/unpublished databases (Proquest, National Institute for Health and Care Excellence, Canadian Health Research Collections, Clinical Trials) were searched from inception to 7 March 2022. Eligibility criteria for selecting studies: Studies involving PCP-delivered physical activity prescriptions or counselling for participants with a chronic disease or mental health condition, which reported clinical outcomes were included. Opinion papers, news and magazine articles and case reports were excluded, as were studies in which a physical activity intervention was provided for primary prevention of chronic disease, prescribed by healthcare providers or researchers other than PCPs, or for healthy participants without chronic disease. Results: An initial search identified 4992 records. Fifteen studies met inclusion criteria. Characteristics of physical activity prescriptions that improved clinical outcomes included: personalised advice; brief intervention; behavioural supports (handouts and/or referrals) and physician follow-up. Reported adverse events were rare. Research gaps include optimal timing and length of follow-up, and the long-term and cost-effectiveness of interventions. Summary/Conclusion: Several characteristics of physical activity counselling by PCPs for patients with chronic disease may improve clinical outcomes, although research gaps remain. Studies exploring the effectiveness of physical activity prescription for individuals with chronic conditions are urgently needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".