Which Learning Activities Enhance Physical Therapist Practice? Part 1: Systematic Review and Meta-analysis of Quantitative Studies
Bibliographic record
Abstract
OBJECTIVE: Following graduation from professional education, the development of clinical expertise requires career-long participation in learning activities. The purpose of this study was to evaluate which learning activities enhanced physical therapist practice. METHODS: Eight databases were searched for studies published from inception through December 2018. Articles reporting quantitative data evaluating the effectiveness of learning activities completed by qualified physical therapists were included. Study characteristics and results were extracted from the 26 randomized controlled trials that met the inclusion criteria. Clinician (knowledge, affective attributes, and behavior) and patient-related outcomes were extracted. RESULTS: There was limited evidence that professional development courses improved physical therapist knowledge. There was low-level evidence that peer assessment and feedback were more effective than case discussion at improving knowledge (standardized mean difference = 0.35, 95% CI = 0.09-0.62). Results were inconsistent for the effect of learning activities on affective attributes. Courses with active learning components appeared more effective at changing physical therapist behavior. The completion of courses by physical therapists did not improve patient outcomes; however, the addition of a mentored patient interaction appeared impactful. CONCLUSION: Current evidence suggests active approaches, such as peer assessment and mentored patient interactions, should be used when designing learning activities for physical therapists. Further high-quality research focused on evaluating the impact of active learning interventions on physical therapist practice and patient outcomes is now needed. IMPACT: This study is a first step in determining which learning activities enhance clinical expertise and practice would enable the physical therapy profession to make informed decisions about the allocation of professional development resources.
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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 | 0.051 | 0.150 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".