Retention of General Practitioners in remote areas of Canada and Australia: A meta‐aggregation of qualitative research
Bibliographic record
Abstract
OBJECTIVE: Our aim was to systematically review qualitative evidence regarding the experiences and perceptions of General Practitioners and the factors influencing retention in remote areas of Canada and Australia. The objectives were to identify gaps and inform policy to improve retention of remote doctors, which should in turn reduce health inequalities for remote communities. DESIGN: Meta-aggregation of qualitative studies of General Practitioners and general practice registrars who had worked in a remote area of Australia or Canada for a minimum of 1 year and/or were intending to stay remote long term in their current placement. RESULTS: Six synthesised findings were identified: peer and professional support, organisational support, uniqueness of remote lifestyle and work, burnout and time off, personal family issues and cultural and gender issues. CONCLUSIONS: Long-term retention of doctors in remote areas of Australia and Canada is influenced by a range of negative and positive perceptions, and experiences with key factors being professional, organisational and personal. All 6 synthesised findings span a spectrum of policy domains and service responsibilities, and therefore, a central coordinating body could be well placed to implement a multifactorial retention strategy.
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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 | 0.054 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".