Future practice location and satisfaction with rural medical education: survey of medical students.
Bibliographic record
Abstract
OBJECTIVE: To explore the desired future practice location of a cohort of medical students with strong rural representation and to inquire whether they were satisfied with their medical experiences in rural primary care settings. DESIGN: Survey questionnaire. SETTING: The College of Medicine at the University of Saskatchewan in Saskatoon. PARTICIPANTS: One hundred twenty-two medical students. MAIN OUTCOME MEASURES: Demographic information, plans for future practice, and opinions on rural medical experiences in primary care settings. RESULTS: Although students from both rural and non-rural backgrounds were highly satisfied with mandatory and voluntary rural experiences and considered them valuable for their medical education, fewer than 10% of the 122 students desired to work in centres with less than 10 000 population. Only 2 students hoped to practise in such locations. Most students interested in family practice were interested in urban practice, and most students from rural areas were not interested in rural practice. CONCLUSION: Both rural and non-rural students were highly satisfied with their medical education in rural primary care settings, but this did not mean either group wanted to practise in rural settings. Demographic profiling of students (to ascertain whether they have rural origins) and assessing satisfaction with rural medical education give only partial information on who might choose to practise family medicine in rural areas.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".