Reaching The ‘Rural Park Bench’ Through The Exercise For Cancer To Enhance Living Well (excel) Study
Bibliographic record
Abstract
INTRODUCTION: Individuals living with and beyond cancer in rural/remote settings face reduced accessibility to exercise programming as a supportive cancer care resource, and as a result report poorer health and lower quality of life. The EXercise for Cancer to Enhance Living Well (EXCEL) study is a hybrid effectiveness-implementation trial that delivers 12-week online and in-person exercise programs for Canadians living with and beyond cancer in rural/remote communities (population < 100,000). EXCEL operates through six provincial hub sites located at large universities across Canada. Understanding the rural/remote reach and consequent demographics is warranted to improve future exercise program implementation. PURPOSE: To assess the rural/remote ‘reach’ of the EXCEL trial alongside participant demographics. Methods: This is a preliminary analysis of the ongoing EXCEL study for reach (guided by the RE-AIM framework) into the rural/remote setting. The great-circle distance from anonymized participant home location (postal code) to their assigned EXCEL central hub site was calculated using the R package gdistance and ggmap. Descriptive characteristics are reported as means and standard deviations. RESULTS: A total of 715 participants out of the current n = 1448 in EXCEL (Mage = 58 ± 14 years; 87% female) had geographic data. The mean distance from participant location to assigned hub site was 327 ± 407 miles. The majority of participants were married (65%), making over $100 k CAD per year (41%), had completed college/university (51%), and were either retired (33%) or on disability (26%). Rural location heat map to be included in presentation of results. CONCLUSIONS: The EXCEL program demonstrates a robust ability to reach underserved rural/remote communities across Canada. The prevalence of retirement or disability status among participants highlights the potential benefits of accessible, home-based interventions, which can improve participation in exercise programs and overall health outcomes for individuals with limited mobility or those living in geographically isolated areas. Future work will examine characteristics of participants, including health-related factors, to inform optimizing delivery of exercise oncology programs to rural/remote individuals living with and beyond cancer. Supported by: CCS-CIHR Team Grant
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".