Location of family medicine graduates' practices. What factors influence Albertans' choices?
Bibliographic record
Abstract
OBJECTIVE: To examine factors that influence family medicine graduates' choice of practice location. DESIGN: Cross-sectional, retrospective survey employing a self-administered, mailed questionnaire. SETTING: Family medicine residency programs at the University of Alberta (U of A) and the University of Calgary (U of C) in Alberta. PARTICIPANTS: Graduates (n = 702) who completed the family medicine residency program at U of A or U of C between 1985 and 1995. MAIN OUTCOME MEASURES: Current practice location; 23 factors influencing current practice location; physicians' sex; community lived in until 18 years of age. RESULTS: Response rate was 63% (442 graduates completed the questionnaire). Overall, the most influential factors in attracting graduates to their current practice locations were spousal influence, type of practice, and proximity to extended family. Type of practice, income, community effort to recruit, medical need in the area, and loan repayments had a substantial influence on family physicians' decisions to practise in rural areas. Male physicians ranked type of practice, whereas female physicians ranked spousal influence, as having the most influence on choice of practice location. Significantly more female than male physicians identified working hours, familiarity with the medical community or resources, and availability of support facilities and personnel as having a moderate or major influence on their decisions. CONCLUSION: Differences between rural and metropolitan residents and between sexes affect family medicine graduates' choices of practice location. These differences should be taken into account in recruitment strategies.
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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 | 0.006 |
| 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 | 0.000 |
| 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".