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Record W2535906841 · doi:10.15453/2168-6408.1196

Knowledge translation and occupational therapy: A survey of Canadian university programs

2016· article· en· W2535906841 on OpenAlexaffabout
Heidi Cramm, Blair Short, Catherine Donnelly

Bibliographic record

VenueThe Open Journal of Occupational Therapy · 2016
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsOccupational therapyKnowledge translationMedical educationDescriptive statisticsMedicinePsychologyKnowledge managementPhysical therapy

Abstract

fetched live from OpenAlex

While Canadian occupational therapy recognizes knowledge translation (KT) as essential to clinical interactions, there has been little attention paid to KT activity in education and research. The objective of this study was to identify the nature of KT activities in which Canadian occupational therapy faculty engage. An electronic survey was sent to faculty at 14 Canadian occupational therapy programs to explore the nature of KT activities, including research, education, strategies, evaluation, and barriers and facilitators. Descriptive statistics were used to analyze the data. Results show that faculty engage in a range of KT activities, with conferences and peer-reviewed publications being the most common. Faculty collaborate frequently with researchers at their institutions and favor both integrated and end-of-grant KT. Collaboration and personal interest were identified as facilitators; time and funding were seen as barriers. Understanding the profile of KT activity across universities creates opportunities for developing institutional and pan-Canadian plans to enhance KT training and capacity.

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)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.258

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.010
Science and technology studies0.0060.002
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.629
GPT teacher head0.529
Teacher spread0.101 · 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 designObservational
DomainMethods
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

Citations6
Published2016
Admission routes2
Has abstractyes

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