Rural Surgical Training in the United States: Delineating Essential Components Within Existing Programs
Bibliographic record
Abstract
BACKGROUND: Rural access to surgical care has reached crisis level. Practicing in rural America offers unique challenges with limited resources and specialists. Most training programs do not provide enough exposure to the endoscopic or the surgical subspecialty skills to prepare a resident for an isolated rural environment. As awareness has increased, many programs have modified curriculum to address this need. The Advisory Council on Rural Surgery (ACRS) of the American College of Surgeons set out to delineate important components of rural training programs and measure to what degree the existing heterogeneous programs contain these components. STUDY DESIGN: The ACRS identified 4 essential components of rural surgical training based on literature and expert opinion. These components included rotations in a rural setting, broad exposure to surgical specialties, endoscopy experience, and lack of competing specialty learners. A list of Accreditation Council for Graduate Medical Education programs from a prior publication was updated with the 2019 Fellowship and Residency Electronic Interactive Database self-identified "rural track" programs, reviewed, and categorized. RESULTS: We identified 39 programs that self-identified as having a rural emphasis. Depending on the extent of which 4 essential components were included, programs were categorized as either "Broad" (12 programs), "Basic" (20 programs), or "Indeterminate" (7 programs). CONCLUSION: The ACRS described the optimal components of a rural surgical training program and identified which components are present in those surgical residencies which self-identified as having a rural focus. This information is valuable to students planning a future in rural surgery and benefits programs hoping to enhance their curriculum to meet this critical need.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".