Exploring cardiologists’ and oncologists’ exercise recommendation and referral practices
Bibliographic record
Abstract
Objective: Exercise is beneficial for individuals who have been diagnosed with cardiovascular disease or cancer. Healthcare providers are well placed to discuss exercise with their patients, but their referral practices and the content of exercise recommendations remain unclear. Method: Cardiologists and oncologists completed an online survey comprising four closed-ended questions and one open-ended question to assess exercise recommendation and referral practices. Chi-square tests were used to compare the frequency of closed-ended responses, and open-ended responses were coded and analysed using qualitative content analysis. Results: Of the 154 surveys, 58 were returned ( n = 25; 43.1% cardiologists, and n = 33; 56.9% oncologists). Respondents ( M age = 45.5 ± 11.1) were mostly men (62.1%). The majority of cardiologists (95.8%) and oncologists (78.1%) reported referring patients to hospital-based exercise programmes. In this study, the cardiologists were more likely to refer patients to certified exercise physiologists (χ 2 (1) = 6.140, p = .021), whereas oncologists were more likely to refer to physical therapists (χ 2 (1) = 11.764, p = .001). Conclusion: Findings reveal that cardiologists and oncologists discussed and recommended exercise to their patients at least some or most of the time; there were differences in the type of exercise professionals they were referred to; and exercise recommendations were variable and infrequently concurred with established guidelines.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".