Advancing the use of theory in occupational therapy: A collaborative process / Promouvoir l’application de la théorie en ergothérapie : un processus de collaboration
Bibliographic record
Abstract
BACKGROUND: Theory is important to the growth and evolution of occupational therapy. However, use of theory remains challenging for many therapists. PURPOSE: The aim was to develop a process that occupational therapists could apply to advance theory in practice. METHOD: Based on a review of the literature and using a qualitative instrumental case study design, 18 student occupational therapists and eight fieldwork educators completed online modules on the theory advancement concepts generated from the literature, wrote journals, and/or participated in online discussions during fieldwork. Following fieldwork, educators were interviewed and students participated in focus groups exploring their experiences. Directed content analysis was used to analyze the data. FINDINGS: Based on the data collected, we developed the Theory Advancement Process (TAP). The TAP is composed of four primary contexts, a climate of collaborative relationships with four key elements, and four essential processes. IMPLICATIONS: The TAP presents a collaborative process for students, faculty, and therapists to work together to advance the use of theory in practice.
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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 | 0.046 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".