Factors perceived to influence rural career choice of urban background family physicians: a qualitative analysis
Bibliographic record
Abstract
BACKGROUND: Urban background physicians are the main source of physician supply for rural communities across Canada. The purpose of this study was to describe factors that are perceived to influence rural career choice and practice location of urban background family medicine graduates. METHODS: We conducted a qualitative, descriptive study employing telephone interviews with 9 urban background family physicians practicing in rural locations. Those who completed residency training between 2006 and 2011, were in rural practice, and had an urban upbringing were asked: when the decision for rural practice was made; factors that influenced rural career choice; and factors that influenced choice of a particular rural location. Emerging themes were identified through content analysis of interview data. RESULTS: We identified four themes as factors perceived to influence rural career choice - variety/broad scope of rural practice, rural lifestyle, personal relationships, and positive rural experience/physician role models. We also identified factors in four areas perceived to influence the choice of a particular rural practice location - having lived in the rural community, spousal influence, personal lifestyle, and comfort with practice expectations. CONCLUSION: Decisions for rural career choice and rural practice location by practicing urban background family medicine graduates are based on clinical practice considerations, training experience, as well as personal and lifestyle factors.
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 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".