Predictors of rural family medicine practice in Canada.
Bibliographic record
Abstract
OBJECTIVE: To examine the attributes of Canadian medical students at matriculation that predicted later practice in a rural location, with the goal of enhancing evidence-based approaches to increasing the numbers of rural family physicians. DESIGN: Demographic, attitudinal, and career choice data were collected from medical students at matriculation. Students were followed prospectively, and these data were linked to postresidency practice location. SETTING: Eight Canadian medical schools. PARTICIPANTS: Study participants were 15 classes of medical students entering medical school between 2002 and 2004. MAIN OUTCOME MEASURES: Backward stepwise logistic regression analysis was used to identify the entry characteristics that predicted postresidency practice as a rural family physician. RESULTS: Data from 1542 students were analyzed. A strong association was found between career interest in rural family medicine at entry into medical school and postresidency rural practice as a family physician. Logistic regression analysis that did not include entry career interest found older age, being in a relationship, having completed school in a rural community, having a societal orientation, and expressing a desire for a varied scope of practice to be predictive of practising in a rural location. When entry career interest in a rural setting was included in the multivariate model, only this variable and older age predicted postresidency rural family practice. CONCLUSION: This study identified a number of demographic and attitudinal variables at medical school entry that predict postresidency practice in a rural setting. These results suggest multiple potential areas where the pipeline to rural family practice can be further supported in order to address the shortage of rural family physicians.
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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".