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Abstract P4-04-09: Supporting the Knowledge Translation of Exercise Recommendations for People with Bone Metastases using Experience-based Co-Design

2025· article· en· W4411289704 on OpenAlexaboutno aff
Diego Malon Gimenez, Kelcey A. Bland, Sarah Neil‐Sztramko, David M. Langelier, Kirstin N. Lane, Alana Chalmers, Rhoda Dinardo, Nicole Prestley, Sarah Weller, Shabbir M.H. Alibhai, L Capozzi, Janet Papadakos, Karen McDonald, Jane Copp, Margaret L. McNeely, Leah K. Lambert, Christine Simmons, Alan Bates, Kristin L. Campbell, Michelle B. Nadler

Bibliographic record

VenueClinical Cancer Research · 2025
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsnot available
Fundersnot available
KeywordsKnowledge translationMedicineTranslation (biology)Knowledge managementComputer science

Abstract

fetched live from OpenAlex

Abstract Background: Historically, people with bone metastases have been advised to limit exercise due to the risk of adverse events, such as fractures. In 2022, the International Bone Metastases Exercise Working Group published evidence-informed recommendations indicating that regular exercise can enhance physical function and quality of life in these patients, and the risk of skeletal events should be weighed against potential health benefits. Barriers remain to the adoption of these recommendations into clinical practice, including lack of knowledge about how to exercise safely and uncertainty where to find guidance for exercise advice. This project aims to collaborate with knowledge users (KUs) through an experience-based co-design (EBCD) process to develop knowledge translation (KT) products and a dissemination plan to facilitate the uptake of exercise recommendations for people with bone metastases. Methods: Three KU groups were engaged using EBCD: 1) patient/family partners; 2) oncology healthcare providers (physicians, nurses, allied health professionals); and 3) community-based exercise professionals (physiotherapists, exercise physiologists). Through facilitated meetings, ideas for KT products were generated, prioritized, and co-created, and a dissemination and evaluation plan was developed. A survey of exercise professionals was used to collect further information on professional development (PD) preferences. Results: Twenty-nine KUs participated in four engagement sessions (n = 10 patient/family partners, n = 9 healthcare providers, and n = 10 exercise professionals). The prioritized KT products for development were: 1) a patient education handout; 2) patient education videos; 3) a health information form (HIF); and 4) professional development (PD) materials for exercise professionals and healthcare providers. The HIF was designed to easily collect pertinent health and oncology-related patient information that would be necessary for an exercise professional when developing exercise prescription/recommendations. 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. Over 80% of respondents found the purpose of the HIF form was easy to understand and over 75% rated the content as excellent or very good. Our dissemination and evaluation plan included a combination of targeted social media, direct connections with key organizations, presentations, and promotion through the Canadian Cancer Society and its channels. All these products are hosted on the Bone Metastases & Exercise (BMD) Hub (bit.ly/BMEhub). In the first 30 days post-launch, the BME Hub had 5,243 views from 2,407 unique users. Full engagement results will be available at the time of conference. Discussion & Conclusion: Collaborating with KUs through EBCD has enabled the development of diverse KT products that are more likely to be useful and acceptable to the intended audience than those designed by researchers alone. Thus far, use of the Hub has been promising; further evaluation of reach, usage, and partnership indicators of the KT products and dissemination plan will be evaluated. Citation Format: Diego Malon Gimenez, Kelcey A. Bland, Sarah E. Neil-Sztramko, David M. Langelier, Kirstin N. Lane, Alana Chalmers, Rhoda Dinardo, Nicole Prestley, Sarah Weller, Shabbir Alibhai, Lauren Capozzi, Janet Papadakos, Karen McDonald, Jane Copp, Margaret McNeely, Leah Lambert, Christine Simmons, Alan Bates, Kristin L. Campbell, Michelle B. Nadler. Supporting the Knowledge Translation of Exercise Recommendations for People with Bone Metastases using Experience-based Co-Design [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P4-04-09.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.081
metaresearch head score (Gemma)0.112
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.430

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0810.112
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0050.004
Open science0.0030.009
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.002

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.

Opus teacher head0.555
GPT teacher head0.627
Teacher spread0.073 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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