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Record W2034274645 · doi:10.1177/000841740407100408

Enhancing Research Use through Online Action Research

2004· article· en· W2034274645 on OpenAlexaffvenue
Mary Egan, Claire‐Jehanne Dubouloz, Susan Rappolt, Helene J. Polatajko, Claudia von Zweck, Judy King, Josée Vallerand, Janet Craik, Jane A. Davis, Ian D. Graham

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2004
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPsychologyAction (physics)Action researchMedicinePedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: Many health professionals, including occupational therapists, have difficulty utilizing research findings in daily practice. PURPOSE: To determine if an online action research project could enhance research use among occupational therapists working in similar practice areas. METHODS: Four groups of 12-14 occupational therapists met online for approximately one year. They discussed barriers and facilitators to research utilization, defined practice questions, and attempted to search for, synthesize and apply relevant research findings. Online communications and post-group interviews were thematically analyzed. RESULTS: Only half of the participants who began the project were still online with the research project at completion. These participants believed that their involvement in the group led to increased personal awareness, motivation and confidence regarding the use of research evidence in practice and knowledge to be used in practice. Time to review, critique and synthesize research evidence continued to be a major barrier to enhanced research utilization. PRACTICE IMPLICATIONS: Online meetings designed to enhance research use among occupational therapists appear to hold some promise, but refinements are needed to ensure their ultimate success.

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)0.010
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.839
GPT teacher head0.672
Teacher spread0.167 · 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.

Study designNot applicable
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

Citations19
Published2004
Admission routes2
Has abstractyes

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