A Cancer Exercise Toolkit Developed Using Co-Design: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Access to exercise therapy for cancer survivors is poor. Professional development to support exercise professionals in delivering these interventions is needed. Few online resources exist for exercise professionals to address this issue. OBJECTIVE: To develop and evaluate a freely available online toolkit to support exercise professionals working with cancer survivors. METHODS: A 2-phase, experience-based co-design approach was used to develop and evaluate the online toolkit. The two phases were as follows: 1) needs identification and co-design of resources and platform and 2) pilot evaluation. Four co-design workshops were conducted, transcribed, and thematically analyzed to identify key elements for the toolkit. For the pilot evaluation, a customized survey (the Determinants of Implementation Behavior Questionnaire) was distributed to exercise professionals at baseline and 3 months after launch of the online toolkit to determine its usability, utility, and effectiveness in improving their knowledge, confidence, and behavior. Results were reported as the median and interquartile range and changes were calculated using non-parametric tests. Website analytics described site usage after the initial evaluation. RESULTS: Twenty-five exercise professionals participated in co-designing 8 key elements of the online Cancer Exercise Toolkit: the homepage and pages for getting started, screening and safety, assessment, exercise prescription, education, locations, and resources. For the pilot evaluation, 277/320 respondents (87% of whom were physiotherapists) from 26 countries completed the survey at baseline, with 58 exercise professionals completing follow-up surveys at 3 months. Exercise professionals' knowledge, skills, and confidence in delivering exercise therapy to cancer survivors increased 3 months after baseline (items 1, 6, and 8: median score 5, IQR 3 to 6) to follow-up (items 1 and 6: median score 6, IQR 5 to 6; item 8: median score 5, IQR 5 to 7; P<.001) on a 1 to 7 Likert scale. Most participants (35/44, 80%) agreed or strongly agreed they would recommend the toolkit to colleagues. In the 6 months following the pilot evaluation, the toolkit received an average of 866 views per month. CONCLUSIONS: The co-designed online Cancer Exercise Toolkit was a useful resource for exercise professionals that may increase their knowledge, skills, and confidence in providing exercise therapy to cancer survivors.
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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 | 0.054 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".