Excellence in Academic Physical Therapy: Promoting a Culture of Data Sharing
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Data analytics are increasingly important in health professions education to identify trends and inform organizational change in rapidly evolving environments. Unfortunately, limitations exist in data currently available to determine physical therapy (PT) academic excellence. It is imperative that the American Council of Academic Physical Therapy (ACAPT) be able to demonstrate data-informed progress in addressing the common challenges faced by Doctor of Physical Therapy programs. POSITION AND RATIONALE: The Task Force to Explore Data and Technology to Evaluate Program Outcomes was convened by ACAPT to explore current and desired data and the needs, technology, and costs that would be required for ACAPT to assess program outcomes relative to excellence criteria. The Task Force performed a gap analysis of measures of excellence, provided evidence-based recommendations for advancing the use of data and technology systems in academic PT, and generated a comprehensive Assessment Excellence Map that subsequently led to a new streamlined Excellence Framework in the launch of the ACAPT Center for Excellence. DISCUSSION AND CONCLUSION: The vision of universal excellence in PT education necessitates clear alignment and centralization of common data to support efficient processes to assess excellence. The transformative nature of data is untapped in PT academic endeavors, and nascent work to establish and sustain a culture of centralized data sharing and assessment will help to drive program-level and profession-level excellence in PT education.
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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 | 0.403 | 0.401 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.017 | 0.038 |
| Scholarly communication | 0.042 | 0.032 |
| Open science | 0.007 | 0.065 |
| Research integrity | 0.006 | 0.026 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".