A meta aggregation of qualitative research on retention of general practitioners in remote Canada and Australia
Bibliographic record
Abstract
OBJECTIVE: Our aim was to systematically review qualitative evidence regarding the experiences and perceptions of general practitioners and what factors influence their retention in remote areas of Canada and Australia. The objectives were to identify gaps and inform policy to improve retention of remote general practitioners, which should in turn improve the health of our marginalised remote communities. DESIGN: Meta-aggregation of qualitative studies. SETTING: Remote general practice in Canada and Australia. PARTICIPANTS: General practitioners and general practice registrars who had worked in a remote area for a minimum of one year and/or were intending to stay remote long term in their current placement. RESULTS: Twenty-four studies were included in the final analysis. A total of 811 participants made up the sample with a length of retention ranging from 2 to 40 years. Six synthesised findings were identified from a total of 401 findings; these were around 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, or personal. All six factors 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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".