Exploring Healthcare Provider Experiences with the EXCEL Exercise Referral Pathway for Individuals Living with and Beyond Cancer
Bibliographic record
Abstract
Exercise is an evidence-based strategy shown to reduce the negative side effects associated with cancer treatment for individuals living with and beyond cancer (LWBC). Healthcare providers (HCPs) play a critical role in promoting exercise for these individuals. Notwithstanding, several barriers hinder HCPs' ability to discuss and support exercise in clinical practice. EXCEL is an exercise intervention designed to address health disparities in access to exercise oncology resources for rural/remote individuals LWBC, including a referral pathway for HCPs to use. The purpose of this study was to evaluate HCP experiences using the EXCEL exercise referral pathway. We employed an interpretive description methodology, using semi-structured interviews to assess HCP experiences with EXCEL. Overall, HCPs felt empowered to refer to exercise when they were supported in doing so. The findings highlighted (1) a need for a better understanding of the role of exercise professionals and their integration into cancer care; (2) the need for efficient referral systems including embedding referrals into existing health care electronic record systems; and (3) sharing patient feedback with exercise oncology programs back to the HCPs to drive continued referrals.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".