Evaluation of a knowledge translation initiative for physical therapists treating patients with work disability
Bibliographic record
Abstract
PURPOSE: We evaluated a knowledge translation and exchange (KTE) initiative aimed at providing physical therapists with best practice information regarding work disability prevention. METHOD: The KTE initiative involved dissemination of a best practice resource guide for work disability prevention, creation of a network of peer-selected educationally influential clinicians, province-wide seminars for practicing clinicians, and use of the resources in an academic training curriculum. Evaluation included email surveys of clinician practice patterns and exposure to the KTE initiative. We also evaluated the impact of the KTE on community physical therapy workers' compensation outcomes. RESULTS: 241 and 164 clinicians responded to the baseline and follow-up email surveys, respectively. Clinicians reported a wide range of years in practice (0-30+ years) and practice settings although the majority worked in private clinics (approximately 61%). Approximately 80% of the follow-up sample reported some exposure to the KTE initiative. Few differences were observed in reported practice patterns between survey periods. Compensation outcomes improved slightly after KTE (68 versus 70% off benefits) however, this effect was confounded by proportion of claimants with sprain/strain injury. CONCLUSIONS: Implementation of a KTE initiative appears to have had little impact on the clinical practice or outcomes. Future interventions for rehabilitation professionals should consider the organisational culture of the settings in which they practice, which may be a barrier or facilitator of research uptake.
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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 | 0.072 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".