Defining Rural: The Predictive Value of Medical School Applicants’ Rural Characteristics on Intent to Practice in a Rural Community
Bibliographic record
Abstract
PURPOSE: To understand the predictive value of medical student application characteristics on rural practice intent. METHOD: The authors constructed a linked database of 2012-2017 medical school matriculants from American Medical College Application Service applications and Association of American Medical Colleges Matriculating Student Questionnaire (MSQ, 2012-2017) and Graduation Questionnaire (GQ, 2016-2018). Using logistic regression, they compared application variables (birth, high school, childhood county, and self-declared geographical origin) to students' MSQ and GQ intent to practice rurally. Rural practice intent from matriculation to graduation was compared using the McNemar test for paired nominal data. RESULTS: The number of students meeting inclusion criteria was 115,027. More students self-declared rural origin (18,662; 16.4%) than were identified using geographically coded variables (6,097-8,784; 6.1%-8.1%). Geographically coded rural variables were all strongly and similarly associated with rural practice intent, with rural high school being the most predictive on both MSQ (odds ratio [OR], 6.51; CI, 6.1-7.0) and GQ (OR, 5.4; CI, 4.9-6.0). Self-declared geographical origin was associated with a similar rural practice intent on both MSQ (OR, 6.93; CI, 6.5-7.3) and GQ (OR, 5.69; CI, 5.2-6.2). Rural practice intent declined for all groups from matriculation to graduation. CONCLUSIONS: Considering students who self-declare as rural identifies a larger group of rural medical school applicants than more "objective" geographic variables, without negatively impacting students' predicted interest in eventual rural practice. Further research should track actual practice location and explore strategies to mitigate declining rural career interest.
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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 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".