Preparedness for rural community leadership and its impact on practice location of family medicine graduates
Bibliographic record
Abstract
OBJECTIVE: To identify non-clinical dimensions of preparedness for rural practice and to determine whether preparedness for rural practice is predictive of rural practice location. DESIGN: Cross-sectional postal survey mailed in 2001. SETTING: Communities across Canada where graduates were practising. SUBJECTS: Graduates (n = 369) of the family medicine residency program at the universities of Alberta (U of A) and Calgary (U of C) between 1996 and 2000, inclusive. INTERVENTIONS: Using a 4-point scale, graduates rated the extent to which the residency program prepared them for eight dimensions of rural practice: clinical demands of rural practice, understanding rural culture, small community living, balancing work and personal life, establishing personal/professional boundaries, becoming a community leader, handling a 'fish bowl' lifestyle, and choosing a suitable community. MAIN OUTCOME MEASURE: Identification of non-clinical dimensions of preparedness for rural practice and whether scores on preparedness scales are predictive of rural practice location. RESULTS: The overall response rate was 76.4%. Factor analysis of the eight preparedness items produced two factors, 'rural culture' and 'rural community leader' which explained 72% of the variance. The alpha coefficient for each factor was 0.87. Odds ratios revealed that family medicine graduates prepared for rural community leadership roles were 1.92 (CI = 1.03-3.61) times more likely to be in rural practice. Rural physicians were also 2.14 (CI = 1.13-4.03) times as likely to have a rural background. CONCLUSIONS: Preparedness to be a rural community leader and having a rural background were predictive of rural practice. Educators should consider this in both family medicine residency admissions policy and practice and when designing and implementing family medicine residency curricula.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".