Practice Style Traits: Do they help explain practice behaviours of stroke rehabilitation professionals?
Bibliographic record
Abstract
RATIONALE: The gap in knowledge translation from research to clinical practice is under scrutiny in stroke rehabilitation. One possible reason for this gap may be a poor understanding of clinicians' practice style traits and how they influence practice behaviours. OBJECTIVES: To identify the prevalence of practice style traits in physical therapists and occupational therapists working in stroke rehabilitation and, to explore associations between these traits and practice behaviours, where practice behaviours are defined as the clinicians' reasons for choosing assessments and interventions used in practice. The influence of more traditional personal and organizational factors on practice behaviours was also explored. DESIGN: Cross-sectional survey of a representative random sample of 243 clinicians (117 occupational therapists and 126 physical therapists) working across the continuum of stroke care in Ontario, Canada. METHODS: A telephone-administered validated clinical practice survey elicited information in 4 areas: practice style traits using the validated Practice Style Questionnaire, therapists' reasons for choosing assessments and interventions (practice behaviours), personal factors and organizational factors. RESULTS: For both disciplines, the most prevalent trait was pragmatist and the least prevalent was seeker. Seekers were the most likely to use evidence-based reasons for choosing assessments, but this finding did not reach significance (chi2 = 5.430, df = 3; p = 0.14). The most typical reason for choosing an intervention was that the clinician had learned it during professional training, an interesting finding given that approximately half of clinicians had more than 10 years of experience. Of the 21 potential explanatory variables examined, few explained clinicians' reasons for choosing assessments or interventions. CONCLUSION: While understanding practice traits is not going to be the single solution to closing the knowledge translation gap, it may help to guide best practice implementation strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".