Challenges and Opportunities in Academic Physiatry
Bibliographic record
Abstract
ABSTRACT: Environmental scans determine trends in an organization's or field's internal and external environment. The results can help shape goals, inform strategic decision making, and direct future actions. The Association of Academic Physiatrists convened a strategic planning group in 2020, composed of physiatrists representing a diversity of professional roles, career stages, race and ethnicity, gender, disability status, and geographic areas of practice. This strategic planning group performed an environmental scan to assess the forces, trends, challenges, and opportunities affecting both the Association of Academic Physiatrists and the entire field of academic physiatry (also known as physical medicine and rehabilitation, physical and rehabilitation medicine, and rehabilitation medicine). This article presents aspects of the environmental scan thought to be most pertinent to the field of academic physiatry organized within the following five themes: (1) Macro/Societal Trends, (2) Technological Advancements, (3) Diversity and Global Outreach, (4) Economy, and (5) Education/Learning Environment. The challenges and opportunities presented here can provide a roadmap for the field to thrive within the complex and evolving healthcare systems in the United States and globally.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".