From continuing education to personal digital assistants: what do physical therapists need to support evidence‐based practice in stroke management?
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Understanding how to structure educational interventions and resources to facilitate physical therapists' application of the research literature is required. The objective of this study was to explore physical therapists' preferences for strategies to facilitate their access to, evaluation and implementation of the stroke research literature in clinical practice. METHODS: In-depth, qualitative telephone interviews were conducted with 23 physical therapists who treat people with stroke in Ontario, Canada and who had participated in a previous survey on evidence-based practice. Data were analysed using a constant comparative approach to identify emergent themes. RESULTS: Participants preferred online access to research summaries or systematic reviews to save time to filter and critique research articles. To enable access in the workplace, an acceptable computer-to-staff ratio, permission to access web sites and protected work time were suggested. Participants considered personal digital assistants as excellent tools for quick access to online resources but were unsure of their advantage over a desktop computer. Therapists favoured use of non-technical language, glossaries of research terms and quality ratings of studies to ease understanding and appraisal. Teleconferencing or videoconferencing overcame geographical but not scheduling barriers to accessing education. To achieve behaviour change in clinical practice, therapists preferred multiple interactive, face-to-face education sessions in a group format, with opportunities for case-based learning and practice of new skills. CONCLUSION: Physical therapists prefer technology-assisted access to resources and education and favour attending multiple interactive, expert-facilitated education sessions incorporating opportunities for case-based learning and practice of new skills to change behaviour related to evidence-based 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 | 0.160 | 0.384 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".