Evaluating West Virginia’s Emergency Medicine Workforce: A Longitudinal Observational Study
Bibliographic record
Abstract
Objective Although the urban emergency workforce is well studied, rural departments are less understood. This study seeks to further define the landscape of rural healthcare and expand on previous studies of the West Virginia (WV) workforce. Methods During the second quarter of 2019, surveys were sent via email to medical directors' professional IDs as anonymous survey links. Hard copies were also sent to directors at their hospital addresses. Responses were aggregated with hospitals stratified based on annual census and rural classification. Data was interpreted through descriptive analysis. Results Surveys were sent to 53 departments with a 55% response rate. Of the responding hospitals, 15 of 29 were identified as rural. The average state-wide annual hospital census was 29,500 visits with board-certified emergency medicine (EM)-trained physicians covering 67% of shifts. Rural departments have a smaller census and less specialized coverage. Full-time physicians are found to have the strongest ties to WV, with 65% attending medical school, residency, or growing up in the state. Conclusion Board-certified EM-trained physicians provide some level of coverage in most emergency departments in WV but remain underrepresented in rural locations. This specialized coverage has increased by 20% in the last 15 years. Additionally, a majority of hospitals have access to basic consulting services (surgery and primary care); however, other specialists are rare in rural WV.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".