Exercise counselling and referral in cancer care: an international scoping survey of health care practitioners’ knowledge, practices, barriers, and facilitators
Bibliographic record
Abstract
Abstract Purpose Evidence supports the role of prescribed exercise for cancer survivors, yet few are advised to exercise by a healthcare practitioner (HCP). We sought to investigate the gap between HCPs’ knowledge and practice from an international perspective. Methods An online questionnaire was administered to HCPs working in cancer care between February 2020 and February 2021. The questionnaire assessed knowledge, beliefs, and practices regarding exercise counselling and referral of cancer survivors to exercise programs. Results The questionnaire was completed by 375 participants classified as medical practitioners (42%), nurses (28%), exercise specialists (14%), and non-exercise allied health practitioners (16%). Between 35 and 50% of participants self-reported poor knowledge of when, how, and which cancer survivors to refer to exercise programs or professionals, and how to counsel based on exercise guidelines. Commonly reported barriers to exercise counselling were safety concerns, time constraints, cancer survivors being told to rest by friends and family, and not knowing how to screen people for suitability to exercise (40–48%). Multivariable logistic regression models including age, gender, practitioner group, leisure-time physical activity, and recall of guidelines found significant effects for providing specific exercise advice ( χ 2 (7) = 117.31, p < .001), discussing the role of exercise in symptom management ( χ 2 (7) = 65.13, p < .001) and cancer outcomes (χ 2 (7) = 58.69, p < .001), and referring cancer survivors to an exercise program or specialist ( χ 2 (7) = 72.76, p < .001). Conclusion Additional education and practical support are needed to equip HCPs to provide cancer survivors with exercise guidelines, resources, and referrals to exercise specialists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".