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Building the future of exercise oncology: current status of international workforce development and integration into standard cancer care

2025· article· en· W4413311674 on OpenAlex
Karen Y. Wonders, Mary A. Kennedy, L Capozzi, Yao Lei, Lervasen Pillay, Fabrí­cio Azevedo Voltarelli, Joachim Wiskemann, Anna Campbell

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJNCI Monographs · 2025
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsWorkforceMedicineCurrent (fluid)CancerWorkforce developmentInternal medicineOncologyEngineeringPolitical science

Abstract

fetched live from OpenAlex

The complex requirements of people with cancer can impact the provision of safe, effective, evidence-based exercise prescription. Consequently, a range of essential competencies are required from the exercise oncology workforce. There is a global need for a standardized approach to the development of this workforce. By defining, standardizing, and training the workforce in essential competencies, this will enable various professionals to safely and effectively screen, access, design, and deliver appropriate exercise programs. Therefore, this is also a call for a global collaboration on the development of the exercise oncology workforce with special attention to assisting low- or middle-income countries with their increasing cancer burden and unique challenges, which may require unique context-specific strategies. The building of an appropriate internationally standardized workforce is essential in the provision of physical activity and exercise options as part of standard cancer care.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.934
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.399
Teacher spread0.386 · 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