Excellence in Academic Physical Therapy: Promoting a Culture of Data Sharing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it