Predictors of rural family medicine practice in Canada.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it