Abstract P4-04-09: Supporting the Knowledge Translation of Exercise Recommendations for People with Bone Metastases using Experience-based Co-Design
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,081 | 0,112 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,003 | 0,009 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».