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Record W4389088678 · doi:10.1007/s10459-023-10298-9

Creating synergies among education/research, practice, and policy environments to build capacity for the scholar role in occupational therapy and physiotherapy in the Canadian context

2023· article· en· W4389088678 on OpenAlexafffundabout
Sung-Ha Kim, Annie Rochette, Sara Ahmed, Philippe S. Archambault, Claudine Auger, Alex Battaglini, Andrew Freeman, Eva Kehayia, Elizabeth Anne Kinsella, Elinor Larney, Lori Letts, Peter Nugus, Marie‐Hélène Raymond, Nancy M. Salbach, Diana Sinnige, Laurie Snider, Bonnie Swaine, Yannick Tousignant‐Laflamme, Aliki Thomas

Bibliographic record

VenueAdvances in Health Sciences Education · 2023
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsToronto Rehabilitation InstituteUniversité de SherbrookeUniversity of TorontoInstitut National d'Excellence en Santé et en Services SociauxMcMaster UniversityCentre Intégré de Santé et de Services Sociaux des LaurentidesCanadian Association of Occupational TherapistsCentre for Interdisciplinary Research in RehabilitationCentre for Advancing Health OutcomesUniversity Health NetworkCentre intégré de santé et de services sociaux de Chaudière-AppalachesMcGill UniversityMcGill University Health CentreInstitut Universitaire en Santé Mentale de QuébecUniversité LavalUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsOccupational therapyContext (archaeology)Medical educationPhysical therapyMedicineGeography

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)0.143
metaresearch head score (Gemma)0.093
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.857
Threshold uncertainty score0.976

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1430.093
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0110.007
Science and technology studies0.0480.056
Scholarly communication0.0660.020
Open science0.0070.056
Research integrity0.0110.015
Insufficient payload (model declined to judge)0.0150.001

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 head0.136
GPT teacher head0.572
Teacher spread0.436 · 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.

Study designTheoretical or conceptual
DomainIncentives
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

Citations4
Published2023
Admission routes3
Has abstractno

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