Strengthening Physical Medicine and Rehabilitation Departmental Research: Insights From the Association of Academic Physiatrists Research Consulting Program
Bibliographic record
Abstract
ABSTRACT: Clinical and translational research is important for health care and the growth of medical specialties. Physical medicine and rehabilitation offers many opportunities for research, but research growth in physical medicine and rehabilitation is lacking, and research resources and productivity vary across academic physical medicine and rehabilitation departments across North America. The Physiatric Research Consulting Program was developed by the Association of Academic Physiatrists to provide customized recommendations to enhance research capacity and productivity in physical medicine and rehabilitation departments. This report outlines the three components of the Physiatric Research Consulting Program, including a previsit needs assessment, an in-person visit, and a postvisit follow-up final report. The report also provides a qualitative assessment of the impact of the Physiatric Research Consulting Program, with general themes of feedback, site visit evaluations, and final report evaluations. The Physiatric Research Consulting Program was found to be valuable in identifying gaps and needs for physical medicine and rehabilitation departments, providing outside perspectives, and energizing faculty toward research growth in their departments.
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.
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 | 0.047 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".