Perceived preparedness for family practice: does rural background matter?
Bibliographic record
Abstract
INTRODUCTION: Rural background and the ability to adjust to rural practice are strong predictors of recruitment and retention of rural physicians. The degree to which rural background and being prepared for practice interrelate may provide insight into efforts aimed at increasing the supply of rural physicians. The purpose of this study was to examine the association between family medicine graduates' rural or urban background and their self-reported preparedness for practice. METHODS: This was a retrospective, cross-sectional survey of family medicine graduates who completed the 2-year family medicine residency program at the University of Alberta or University of Calgary from 2001 to 2005. Self-rated preparedness was examined on a 4-point Likert scale for 18 elements of clinical family practice, 8 interdisciplinary issues, 10 practice management issues and 8 nonclinical aspects of family practice. Rural background was defined as having been brought up mainly in a rural community (population < 25 000), and urban background was defined as having been brought up mainly in an urban community (population ≥ 25 000). RESULTS: A significantly greater proportion of rural-than urban-background graduates felt prepared for 3 nonclinical aspects of rural practice: time demands of rural practice (95.0% v. 79.3%, p = 0.03), understanding rural culture (92.5% v. 70.2%, p = 0.005) and small-community living (92.5% v. 70.2%, p = 0.003). CONCLUSION: Rural background was associated with physicians feeling prepared for the nonclinical and cultural aspects of rural family practice, which suggests that focused rural exposure facilitates an understanding of rural culture. Urban-background physicians were reportedly less prepared for the nonclinical aspects of rural practice. Increased exposure of urban-background residents to the cultural aspects of rural practice may improve recruitment and retention 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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".