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Growing the Workforce in Oncology Physical Therapy: From Entry Level to Specialist Care

2022· article· en· W4205721423 on OpenAlex
Colleen Dunphy, Margaret L. McNeely

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueRehabilitation Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal Disorders and Rehabilitation
Canadian institutionsAlberta Cancer FoundationUniversity of AlbertaPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsWorkforceMedicineRehabilitationFamily medicineHealth careCancer therapyNursingGerontologyLibrary scienceCancerPhysical therapyPolitical scienceInternal medicine

Abstract

fetched live from OpenAlex

1Clinical Research Coordinator III/Rehabilitation Consultant, Princess Margaret Cancer Centre—University Health Network, Toronto, Ontario, Canada 2Professor, Department of Physical Therapy, University of Alberta, Edmonton, Alberta 3Supportive Care, Cancer Care Alberta, Edmonton, Alberta. Correspondence: Colleen Dunphy, MSc, BScPT, Princess Margaret Cancer Centre—University Health Network, 700 University Ave, 2N WS71, Toronto, ON M5G 1X6, Canada ([email protected]). The authors declare no conflicts of interest. Online Publication date: January 3, 2022

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.029
GPT teacher head0.354
Teacher spread0.325 · 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