Attributes of evidence-based occupational therapists in stroke rehabilitation
Bibliographic record
Abstract
BACKGROUND.: A better understanding of the features characterizing expert evidence-based occupational therapists in stroke rehabilitation is needed to inform the design of educational and knowledge translation interventions aimed at addressing research-practice gaps. PURPOSE.: The study aimed to identify the attributes of evidence-based occupational therapy stroke rehabilitation experts from the perspective of their peers. METHOD.: Forty-six occupational therapy clinicians and managers completed an online questionnaire asking them to nominate "outstanding" and "expert evidence-based" occupational therapists in stroke rehabilitation and to explain their choices. A thematic analysis of respondents' statements was conducted. FINDINGS.: Both outstanding and expert evidence-based occupational therapists were perceived to be motivated self-learners; to have extensive knowledge, skills, and experience; to act as scholarly practitioners; to achieve superior client outcomes; and to work in specialized settings. IMPLICATIONS.: The development of future strategies supporting occupational therapy students and clinicians to become lifelong learners should take into account key attributes of expertise, such as motivation for continuous learning and professional development.
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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 | 0.030 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".