Factors influencing medical students' choice of family medicine: effects of rural versus urban background.
Bibliographic record
Abstract
OBJECTIVE: To identify factors that influence medical students' choice of family medicine versus another specialty and to analyze influential factors by urban versus rural background of students. DESIGN: Cross-sectional questionnaire survey conducted in 2010. SETTING: University of Alberta in Edmonton. PARTICIPANTS: A total of 118 first-, 120 second-, and 107 third-year medical students. MAIN OUTCOME MEASURES: Twenty-two factors influencing preferred career choice, type of community lived in (rural vs. urban), and student age and sex. RESULTS: Overall, 283 (82.0%) students responded to the survey. Those who preferred family medicine rather than another specialty as a career option were older (≥ 25 years) (69.6% vs. 40.9%, P < .001), female (69.6% vs. 39.3%, P < .001), and had previously lived in rural locations (< 25,000 population) (46.8% vs. 23.9%, P < .001). Four factors were significantly associated with students preferring family medicine compared with any other specialty: emphasis on continuity of care (87.3 vs. 45.3%, P < .001); length of residency (73.4% vs. 25.9%, P < .001); influence of family, friends, or community (67.1% vs. 50.2%, P = .011); and preference for working in a rural community (41.8% vs. 10.9%, P < .001). For students with urban backgrounds, the preference for family medicine was more strongly influenced by the opportunity to deal with a variety of medical problems; current debt load; and family, friends, or community than for those with rural backgrounds. Practice location preferences also differed between students from rural and urban backgrounds. CONCLUSION: Medical students who prefer family medicine as a career choice appear to be influenced by a different set of factors than those who prefer other specialties. Being female; being older; having previously lived in a rural location; placing importance on continuity of care; desire for a shorter residency; and influence of family, friends, or community are associated with medical students preferring family medicine. Some differences in factors influencing career choice exist between medical students from rural versus urban backgrounds. To increase the supply of family physicians, medical schools might consider introducing elements into the admissions process and the medical curriculum that encourage family medicine as a career choice.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".