The difference between medical students interested in rural family medicine versus urban family or specialty medicine.
Bibliographic record
Abstract
OBJECTIVE: To determine how first-year medical students interested in rural family medicine in Canada differ from their peers. METHOD: From 2002 to 2004, first-year students (n = 2189) from 16 classes in 8 Canadian medical schools ranked intended career choices and indicated influences on their choices using Likert scales. We used t tests and chi2 tests to determine demographic influences and factor analysis, and we used analysis of variance to examine associated attitudes. RESULTS: Of the 1978 surveys returned (90.3%), 1905 were used in the analysis. Rural family medicine was ranked first by 11.1%, varying from 4.7% to 20.2% among schools. Students interested in rural family medicine were more likely to have grown up rurally, graduated from a rural high school and have family in a rural location than others (p < 0.001). They were more likely to be older, in a relationship, to have volunteered in a developing nation and less likely to have university-educated parents than those interested in a specialty (p < 0.008). Attitudes of students choosing family medicine, rural or urban, include social orientation, preference for a varied scope of practice and less of a hospital orientation or interest in prestige, compared with students interested in specialties (p < 0.001). CONCLUSION: Medical schools may address the rural physician shortages by considering student demographic factors and attitudes at admission.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".