Performance on the <scp>American Board of Physical Medicine and Rehabilitation</scp> certifying examinations: Rural and urban physicians
Bibliographic record
Abstract
BACKGROUND: Over 60 million people in the United States live in a rural community making up approximately 20% of the population. Data are minimal about the physiatrists who serve this rural population, their performance on certification examinations and how the American Board of Physical Medicine and Rehabilitation (ABPM&R) serves their ongoing educational, assessment, and practice needs. OBJECTIVE: To compare the performance of rural and urban physicians on the Part I, Part II, and maintenance of certification (MOC) examinations along with subspecialty preference and continuance of primary certification. DESIGN: Retrospective cross-sectional study. SETTING: Board-eligible PM&R physicians and certified diplomates of the ABPM&R. PARTICIPANTS: Physicians who participated in an initial certification or maintenance of certification examination with the ABPM&R between 2010 and 2019. METHODS: Comparisons of physician pass rates, mean scaled scores (aggregates), and program pass rates on ABPM&R certifying examinations were completed. Cross-reference to national database and ABPM&R practice site zip codes provided sociogeographic linkage. INTERVENTIONS: Not applicable MAIN OUTCOME MEASURES: Physician mean scaled scores, pass rates, subspecialty preferences, and primary certification status. RESULTS: There were no meaningful differences in performance on the ABPM&R Part I, II, and MOC examinations between rural and urban physiatrists. Most common subspecialty is the pain medicine certification whose diplomates most frequently drop their primary certification. Pediatric rehabilitation medicine certification is rare in rural localities and a health care disparity. CONCLUSION: The study found no meaningful differences in the performance of rural and urban physicians on the ABPM&R certifying examinations.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".