Mobilizing exercise recommendations for people with bone metastases: An experience-based co-design approach.
Bibliographic record
Abstract
e23241 Background: People with bone metastases have historically been advised to limit exercise due to potential risks, such as fractures. Although published exercise recommendations by the International Bone Metastases Exercise Working Group emphasize regular exercise may benefit people with bone metastases, adopting guidelines into clinical practice remains challenging. Our study aims to use an experience-based co-design (EBCD) approach to collaborate with knowledge users (KUs) to develop knowledge mobilization (KM) products and a dissemination plan to promote the adoption of exercise recommendations for people with bone metastases. Methods: Three KU groups participated in EBCD: patient/family partners, oncology healthcare providers (e.g., physicians, nurses, allied health), and community-based exercise professionals (e.g., physiotherapists, exercise physiologists). Through four facilitated meetings, KM products were co-created and a dissemination strategy was planned. A survey of exercise professionals was used to collect further information on professional development (PD) preferences. Results: Twenty-nine KUs from five Canadian provinces participated (n = 10 patient/family partners, n = 9 healthcare providers, and n = 10 exercise professionals). KM products prioritized for co-development were: 1) a patient education handout; 2) patient education videos; 3) a clinical communication tool; and 4) PD resources for exercise professionals and healthcare providers. Finalized KM products will be presented at the meeting. A total of 183 international exercise professionals completed the PD survey, with 80% indicating that more PD resources were needed for bone metastases, focusing on exercise safety, feasibility, and prescription considerations. The dissemination plan will involve partnerships with Canadian and international organizations. Conclusions: Collaborating with KUs through EBCD may enhance KM product development and maximize utility and acceptability compared to researcher-designed products. Our KM products, tailored to support people with bone metastases to exercise safely, may also increase healthcare providers' and exercise professionals' satisfaction by improving the quality of information they can offer patients. A fulsome evaluation is planned to assess the reach, use, and partnership indicators of the KM products and dissemination plan.
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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 | 0.031 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".