Implementation of High-Value Care for Physical Therapy Residents Through Systems-Based Practice Curriculum Development: Case Report
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Systems-based practice is a core competency for physical therapy residents, best acquired through experiential learning. Peer health professions are further along than physical therapy in implementing curricula that support systems-based practice. Clinical and practice data in residency programs could provide for education in high-value care (HVC) as a foundation for systems-based practice. Our purpose was to develop and assess a HVC curriculum incorporating reflective practice to help residents achieve competency in systems-based practice. CASE DESCRIPTION: The Logic Model, which evaluates key components needed for success and sustainability, was used to identify resources for a curriculum in HVC. Two orthopedic physical therapy residents and 5 faculty mentors participated in didactic and mentoring sessions. A practice dashboard for each clinician was developed to facilitate resident-mentor discussions. Focus group input was used to refine the curriculum. The validated Systems Thinking Scale, the Quality Improvement Knowledge Application Tool Rubric, and the American Physical Therapy Association Residency Core Competency Score were used to assess residents' progress and to make comparisons to prior years' residents. OUTCOMES: The residents demonstrated increases in systems thinking and quality-improvement knowledge and improvements in clinical outcomes and practice efficiencies. Three themes emerged from semistructured interviews: challenges to HVC, current approach in HVC, and future-oriented thinking in HVC in practice. DISCUSSION AND CONCLUSION: This study demonstrates that HVC activities and a personalized clinical dashboard in a physical therapy residency program can facilitate experiential learning of systems-based practice, a core competency for value-centered, inclusive 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".