Professional, personal and community: 3 domains of physician retention in rural communities.
Bibliographic record
Abstract
INTRODUCTION: We sought to explore the professional, personal and community domains of physician retention in 4 rural communities in Alberta and to develop a preliminary framework for physician retention. METHODS: We used a qualitative, collective case study design to study 4 rural communities (cases) in Alberta that retained family physicians for 4 years or longer. Participants included physicians, staff members, spouses and community members. Data collected from interviews, documents and observations were analyzed individually, and similarities and differences across all cases were assessed. RESULTS: A range of factors that could influence physicians' decisions to stay in a particular community were described by participants. Within the professional domain, physician supply, physician dynamics, scope of practice and practice set-up were common across all communities, and innovation, and management and support emerged from some communities. The personal factors, goodness-of-fit, individual choice, and spousal and family support were present in all communities. Four community factors--appreciation, connection, active support, and physical and recreational assets--emerged across all communities, and reciprocity was present in 3 communities. From these data, we developed a preliminary retention framework. CONCLUSION: Physicians, policy-makers and community members are encouraged to consider the 3 retention domains of professional, personal and community.
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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 | 0.003 | 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 | 0.005 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".