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Record W2325169429 · doi:10.15453/2168-6408.1189

Knowledge Translation Activities in Occupational Therapy Organizations: The Canadian Landscape

2016· article· en· W2325169429 on OpenAlexaffabout
Catherine Donnelly, Heidi Cramm, Amanda Mofina, Marie‐Ève Lamontagne, Shalini Lal, Heather Colquhoun

Bibliographic record

VenueThe Open Journal of Occupational Therapy · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsUniversity of TorontoUniversité LavalMcGill UniversityQueen's University
Fundersnot available
KeywordsOccupational therapyKnowledge translationMedicinePublic relationsPolitical scienceKnowledge managementPhysical therapy

Abstract

fetched live from OpenAlex

Despite acknowledging the importance of knowledge translation (KT), the occupational therapy profession has demonstrated only emerging KT activity. Organizations are seen as playing an important role in supporting KT. To date, there have been no known attempts to explore KT activities conducted by occupational therapy organizations in Canada. The purpose of this study was to identify and describe KT activities occurring in Canadian occupational therapy organizations. An environmental scan was used to identify KT activities. The websites of occupational therapy national and provincial associations and/or regulatory bodies and the educational programs were searched. A Knowledge Mobilization Matrix (KMM) website was applied to each organizational website. The total KMM scores were highest for universities and lowest for regulatory organizations. The type and nature of the KT activities varied according to the type of organization. Canadian occupational therapy leadership organizations play an important role in supporting KT.

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)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.846

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.016
Science and technology studies0.0220.006
Scholarly communication0.0090.003
Open science0.0020.005
Research integrity0.0010.001
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 head0.458
GPT teacher head0.553
Teacher spread0.094 · 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 designQualitative
DomainEvaluation
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

Citations12
Published2016
Admission routes2
Has abstractyes

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