Perspectives on how evidence‐based practice changes over time: A qualitative exploration of occupational therapy and physical therapy graduates
Bibliographic record
Abstract
RATIONALE: The integration of evidence-based practice (EBP) into rehabilitation education programs has been widespread, but little is known about how graduates' EBP competencies evolve over time. AIMS AND OBJECTIVES: To explore how and why the use of EBP by occupational therapists (OTs) and physical therapists (PTs) evolves during the first 3 years of clinical practice. METHOD: We used an interpretive description methodology. We conducted semi-structured interviews with OTs and PTs who participated in a minimum of three out of four time points in a previous longitudinal pan-Canadian mixed methods study. Data analysis was guided by Braun and Clarke's approach to thematic analysis. RESULTS: Seventeen clinicians (13 OTs and 4 PTs) participated in the study. Our analysis identified six overarching themes: (1) evolution of "what EBP is and what it means"; (2) over time, evidence takes a back seat; (3) patients and colleagues have a vital and perennial role in clinical decision making; (4) continuing professional development plays a vital role in EBP; (5) personal attitudes and attributes influence EBP; and (6) organizational factors influence EBP. CONCLUSION: Our study highlights the dynamic nature of EBP use among OTs and PTs in the first 3 years of clinical practice, emphasizing the need for contextualized approaches and ongoing support to promote evidence-informed healthcare in rehabilitation.
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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 | 0.032 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".