Stakeholders’ priorities in the development of evidence-based practice competencies in rehabilitation students: a nominal group technique study
Bibliographic record
Abstract
PURPOSE: Clinically integrated teaching (CIT) is an effective approach for promoting evidence-based practice (EBP) competencies among medical students. Challenges towards the implementation of CIT in rehabilitation curricula include educators' different conceptualizations of EBP, the perceived complexity of EBP and the boundaries between the academic and the clinical setting. This study aimed to identify tailored strategies to implement in rehabilitation programs and their affiliated clinical sites to support the development of EBP competencies among students in occupational therapy (OT), physical therapy (PT) and speech-language pathology (S-LP). MATERIALS AND METHODS: = 8). RESULTS: The top two strategies identified in the OT/PT NGT were: 1) Developing a flexible definition of EBP that recognizes its complexity; 2) Providing clinicians with more access to the teaching content by pairing faculty with preceptors. The top two strategies identified in the S-LP NGT were: 1) Providing students with opportunities for decision-making with experienced clinicians; 2) Increasing interactions between faculty and preceptors using formal group meetings. CONCLUSION: Findings laid foundations for future integrated knowledge translation projects to collaboratively implement, and test identified strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".