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Record W4396656729 · doi:10.31189/2165-7629-13-s2.525

KNOWLEDGE PRODUCTS TO HELP EXERCISE PROFESSIONALS IMPLEMENT EXERCISE RECOMMENDATIONS FOR PEOPLE WITH BONE METASTASES: CO-DESIGN OF A HEALTH INFORMATION FORM

2024· article· en· W4396656729 on OpenAlexaff
Kirstin N. Lane, Kelcey A. Bland, Sarah Neil‐Sztramko, Michelle B. Nadler, David M. Langelier, Alana Chalmers, Rhoda Dinardo, Nicole Prestley, Sarah Weller, Shabbir M.H. Alibhai, L Capozzi, Janet Papadakos, Karen A. McDonald, Jane Copp, Margaret L. McNeely, Christine Simmons, Leah K. Lambert, Alan Bates, Kirstin L Campbell

Bibliographic record

VenueJournal of Clinical Exercise Physiology · 2024
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsUniversity of AlbertaB.C. Women's Hospital & Health CentrePrincess Margaret Cancer CentreMcMaster UniversityCanadian Cancer SocietyUniversity Health NetworkUniversity of British ColumbiaUniversity of CalgaryUniversity of Victoria
Fundersnot available
KeywordsHealth professionalsBone healthMedicineMedical educationPhysical therapyPsychologyHealth carePathology

Abstract

fetched live from OpenAlex

INTRODUCTION People with bone metastases have historically been advised to limit exercise due to risk of skeletal adverse events, such as bone fracture. Published recommendations by the International Bone Metastases Exercise Working Group (IBMEWG) emphasize regular exercise may benefit people with bone metastases; however, there are barriers to adopting these guidelines into clinical practice. For example, exercise professionals (EPs) report challenges obtaining necessary medical information about bone metastases to develop/design safe exercise programs. To address this, a Health Information Form (HIF) was developed using an experience-based co-design approach with three knowledge user (KU) groups: patient/family partners (PFPs), oncology healthcare providers (HCPs), and EPs. The aim of this study was to gather feedback from an international audience on the usability of the HIF. METHODS An online survey was advertised widely to KU groups using a social media toolkit and targeted email invitations to professional organizations. Questions included demographic information, usability of the HIF, and suggestions for dissemination. Analysis of survey responses was descriptive. RESULTS 69 respondents from North America (71%), Europe (20%), and Australasia (9%) provided feedback on the HIF. Most were EPs (54%) or PFPs (36%), and half (54%) had some awareness of the IBMEWG exercise recommendations. 81% of respondents found the purpose of the HIF easy to understand and 77% rated the content as excellent or very good. The design was rated as excellent or very good by 62% with feedback such as “… covers a lot of areas in a way that is brief and effective”. 14 respondents suggested information that could be added to the HIF (e.g., fracture risk factors, recent surgeries). CONCLUSION Overall, the HIF was well received by an international audience. The feedback provided by respondents will be utilized to enhance both the design and content of the form.

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)0.022
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.052
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0100.003

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 head0.156
GPT teacher head0.485
Teacher spread0.329 · 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 designNot applicable
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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