Theory of Research Utilization Enhancement: A Model for Occupational Therapy
Bibliographic record
Abstract
BACKGROUND: There is a pressing need for occupational therapists to provide research-based practice, yet there is little understanding of the specific strategies and processes individual practitioners use to integrate research evidence into their clinical practice. METHOD: Using grounded theory method, the self-reported research utilization strategies of a sample of 11 elite occupational therapists practicing in adult stroke rehabilitation were examined. The triangulation of the interview data, the organizational policies of their workplaces, and existing theoretical concepts and processes of research utilization resulted in the development of a theory and a practice model to guide research utilization in occupational therapy. RESULTS: The Theory of Research Utilization Enhancement for Occupational Therapists, and the Model of Research Utilization in Occupational Therapy are presented, and their implications for practice, policy, education and future research are discussed. PRACTICE IMPLICATIONS: Built upon the Occupational Performance Process Model, the theory and model are proposed as guides to enhance therapists' ability to maintain a client-centred approach while informing clinical practices with research evidence. The application of structured reflection, case application and peer consultation facilitate the integration of research evidence into clinical practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.058 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".