Development of a Novel Evidence-Based Practice-Specific Competency for Doctor of Physical Therapy Students in Clinical Education: A Modified Delphi Approach
Bibliographic record
Abstract
INTRODUCTION: Evidence-based practice (EBP) results in high-quality care and decreases unwarranted variation in practice. REVIEW OF THE LITERATURE: Few performance criteria related to EBP are included in physical therapy clinical education (CE) performance measures, despite EBP requirements in Doctor of Physical Therapy education. The purpose of this study was to develop EBP-specific competencies that may be used for Doctor of Physical Therapy students for use throughout CE. SUBJECTS: Thirteen subject matter experts (SME) participated in this study. METHODS: Subject matter experts were asked to rank each core EBP competency, from a previously described framework, using a 3-point Likert scale, which included "Not Essential," "Essential," and "Not Sure." A consensus of 70% or greater for the "Essential" rating advanced the competency to the final Delphi round, whereas a consensus of 70% or greater for the "Not Essential" rating was required for competency elimination. Subject matter experts voted to either "Accept" or "Modify" the competencies that had reached the inclusion consensus threshold. All competencies that reached consensus for inclusion after all 3 rounds were included in the final EBP Domain of Competence. RESULTS: Consensus was achieved in round one for 38% ( n = 26) of items. In round 2, a consensus was achieved for 20% ( n = 8) of items. Of the items remaining after rounds 1 and 2, 6 overarching competencies were identified, and all remaining items served as descriptions and specifications in the final EBP Domain of Competence. DISCUSSION AND CONCLUSIONS: The 6 competencies developed from this study constitute the EBP Domain of Competence and may be used throughout CE to assess students' EBP competency in clinical 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 | 0.110 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".