Prescribing Exercise for Cardiac Patients: Knowledge, Practices, and Needs of Family Physicians and Specialists
Bibliographic record
Abstract
PURPOSE: To determine the following about prescribing exercise for cardiac patients: physicians' present and needed knowledge; their present practices; barriers that hinder them; and perceived need for and content of a protocol for prescribing exercise. METHODS: (1) Questionnaire mailed to 371 family physicians (FPs), 31 internists, and 25 cardiologists; and (2) four focus groups consisting of 25 FPs, 1 internist, and 3 cardiologists. RESULTS: Questionnaire response rate was 45% (n = 192). Because responses were similar and the group was small, internists and cardiologists were combined as "specialists." Generally, questionnaire data agreed with focus group data, with the latter providing more detail. Family physicians perceived they know little about prescribing a specific exercise program while specialists perceived they know little about motivating patients to begin an exercise program. The method most frequently used by both physician groups to increase exercise is providing general advice. The main barriers to prescribing exercise were inadequate knowledge (FPs only), patient education materials, and community resources. Both groups rated highly the need for a protocol for prescribing exercise and indicated it should: (1) include identification of patient's stage of change; (2) include indications and contraindications for exercise; (3) provide guidelines for developing a specific exercise prescription; (4) contain patient education materials, and (5) be simple and short. CONCLUSIONS: Family physicians perceive they know little about prescribing a specific exercise program for cardiac patients while specialists perceive they know little about motivating patients. Physicians rate highly the need for a protocol to help them prescribe exercise for cardiac patients.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".