Activating cancer communities through an exercise strategy for survivors: an effectiveness-implementation trial
Bibliographic record
Abstract
Introduction Strong evidence supports the recommendation that individuals living with or beyond cancer (LWBC) should be physically active and engage in physical exercise to enhance health and improve cancer-related outcomes. Many individuals LWBC are not achieving these benefits, partly due to a lack of resources. To address this, Activating Cancer Communities through an Exercise Strategy for Survivors (ACCESS) was developed to provide exercise programming and investigate exercise strategies and barriers for those LWBC. Methods Using an effectiveness-implementation design, adults LWBC joined ACCESS by healthcare provider or self-referral. A clinical exercise physiologist triaged participants to either a hospital-based site or one of two community-based sites to complete a 12-week, 24-session multimodal individualized exercise program. Physical fitness and multiple patient-reported outcomes were measured pre- and post-intervention. Results Between January 2018 and March 2020, there were 332 referrals. Of these, 122 participants consented and completed the study. Completing ACCESS was associated with improvements in physical fitness and participant-reported outcomes, including general wellbeing, fatigue, negative emotional states, sleep quality, and exercise self-efficacy. The program was well-received by participants and was deemed feasible and acceptable from an implementation perspective. Discussion The ACCESS program demonstrably improved several health outcomes for individuals LWBC. Implementation outcomes have and continue to guide ongoing efforts to improve accessibility to ACCESS and work with the regional health authority and cancer care program to support the adoption of exercise into standard oncology care. Clinical trial registration clinicaltrials.gov , identifier [NCT03599843].
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".