Baseline Characteristics of Participants in the Alberta Cancer Exercise Hybrid Effectiveness–Implementation Study: A Wake-Up Call for Action
Bibliographic record
Abstract
Background: Alberta Cancer Exercise (ACE) is a hybrid effectiveness–implementation study evaluating a cancer-specific community-based exercise program across urban sites in Alberta, Canada. The purpose of this paper is to describe the baseline characteristics of participants. Methods: Adults with any type and stage of cancer, who were undergoing cancer treatment or up to three years post treatment completion, were eligible. ACE was delivered in person at 18 sites across 7 cities in Alberta, with video conferencing introduced during the COVID-19 pandemic. Participants took part in 60 min of mild-to-moderate intensity exercise twice weekly for a 12-week period and were encouraged to increase overall physical activity. Results: From January 2017 to February 2023, 2570 individuals enrolled. Participants were a mean age of 57.8 years, 71.3% were female, 45.4% had breast cancer, and 49.4% were undergoing cancer treatment. At baseline, only 22.4% of participants self-reported meeting recommended physical activity levels, 66.0% were overweight/obese, and 71.4% reported one or more comorbidities. Most participants were below normative levels for the six-minute walk and 30 s sit-to-stand tests, and 75.9% reported fatigue. Conclusion: Participants were largely inactive, unfit, and symptomatic. ACE attracted more females and individuals with breast cancer but was otherwise representative of the Alberta cancer population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".