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Record W2462608708 · doi:10.1080/07380577.2016.1192311

Knowledge Translation from Research to Clinical Practice: Measuring Participation of Children with Disabilities

2016· article· en· W2462608708 on OpenAlexafffund
Yunwha Jeong, Mary Law, Carol DeMatteo, Paul W. Stratford, Il Hwan Kim

Bibliographic record

VenueOccupational Therapy In Health Care · 2016
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health Research
KeywordsKnowledge translationOccupational therapyMedical educationIntervention (counseling)PsychologyFocus groupClinical PracticeProcess (computing)MedicineNursingKnowledge managementSociologyComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

This knowledge translation project was conducted to increase occupational therapy practitioners' awareness of the importance of measuring participation of children with disabilities. The Knowledge to Action process framework (KTA framework) guided knowledge translation via a web-based seminar (webinar) to practitioners working with children and educators teaching in occupational therapy programs in South Korea. Two hundred and seventy six views of the webinar were recorded within a month and 15 practitioners and 13 educators completed the online evaluation survey. The participants indicated that the webinar helped them understand the participation concept and its associated measures and raised awareness of practitioners' current use of measurement and intervention that do not focus on participation of children with disabilities. This project led practitioners and educators to realize the importance of measuring participation of children with disabilities and contributes to inform the importance of knowledge translation science to facilitate the evidence-based practice.

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 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)0.007
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.628
GPT teacher head0.652
Teacher spread0.024 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations9
Published2016
Admission routes2
Has abstractyes

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