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Record W4405403489 · doi:10.1007/s10926-024-10261-4

What are the Perceptions and Lived Experiences of Canadian Injured Workers about the Provision of Physiotherapy Services using Telerehabilitation?

2024· article· en· W4405403489 on OpenAlexafffundabout
Anne Hudon, Maxi Miciak, Teri Slade, Stacey Lovo, Jackie L. Whittaker, Daniel Côté, Debbie Feldman, Dahlia Kairy, Marie Laberge, Beverley McKeen, Lynn Cooper, Douglas P. Gross

Bibliographic record

VenueJournal of Occupational Rehabilitation · 2024
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsCanada Auto WorkersInstitut de recherche Robert-Sauvé en santé et en sécurité du travailResearch CanadaUniversity of British ColumbiaUniversity of SaskatchewanUniversity of AlbertaUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
FundersWorkSafeBCWorkers' Compensation Board – Alberta
KeywordsTelerehabilitationThematic analysisRehabilitationTelemedicineMedicineTelehealthPsychological interventionKnowledge translationQualitative researchHealth careMedical educationPhysical therapyNursingComputer scienceKnowledge managementPolitical scienceSociology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.005
metaresearch head score (Gemma)0.018
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.379

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0180.011
Scholarly communication0.0100.003
Open science0.0030.005
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.039
GPT teacher head0.414
Teacher spread0.375 · 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".

Quick stats

Citations1
Published2024
Admission routes3
Has abstractno

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