Retaining graduates of non-metropolitan medical schools for practice in the local area: the importance of locally based postgraduate training pathways in Australia and Canada
Bibliographic record
Abstract
INTRODUCTION: The objective of this study was to identify commonalities between one regionally based medical school in Australia and one in Canada regarding the association between postgraduate training location and a doctor's practice location once fully qualified in a medical specialty. METHODS: Data were obtained using a cross-sectional survey of graduates of the James Cook University (JCU) medical school, Queensland, Australia, who had completed advanced training to become a specialist (a 'Fellow') in that field (response rate = 60%, 197 of 326). Medical education, postgraduate training and practice data were obtained for 400 of 409 (98%) fully licensed doctors who completed undergraduate medical education or postgraduate training or both at the Northern Ontario School of Medicine (NOSM), Ontario, Canada. Binary logistic regression used postgraduate training location to predict practice in the school's service region (northern Australia or northern Ontario). Separate analyses were conducted for medical discipline groupings of general/family practitioner, general specialist and subspecialist (JCU only). RESULTS: For JCU graduates, significant associations were found between training in a northern Australian hospital at least once during postgraduate training and current (2018) northern Australian practice for all three discipline subgroups: family practitioner (p<0.001; prevalence odds ratio (POR)=30.0; 95% confidence interval (CI): 6.7-135.0), general specialist (p=0.002; POR=30.3; 95%CI: 3.3-273.4) and subspecialist (p=0.027; POR=6.5; 95%CI: 1.2-34.0). Overall, 38% (56/149) of JCU graduates who had completed a Fellowship were currently practising in northern Australia. For NOSM-trained doctors, a significant positive effect of training location on practice location was detected for family practice doctors but not for general specialist doctors. Family practitioners who completed their undergraduate medical education at NOSM and their postgraduate training in northern Ontario had a statistically significant (p<0.001) POR of 36.6 (95%CI: 16.9-79.2) of practising in northern Ontario (115/125) versus other regions, whereas those who completed only their postgraduate training in northern Ontario (46/85) had a statistically significant (p<0.001) POR of 3.7 (95%CI: 2.1-6.8) relative to doctors who only completed their undergraduate medical education at NOSM (28/117). Overall, 30% (22/73) of NOSM's general speciality graduates currently practise in northern Ontario. CONCLUSION: The findings support increasing medical graduate training numbers in rural underserved regions, specifically locating full specialty training programs in regional and rural centres in a 'flipped training' model, whereby specialty trainees are based in rural or regional clinical settings with some rotations to the cities. In these circumstances, the doctors would see their regional or rural centre as 'home base' with the city rotations as necessary to complete their training requirements while preparing to practise near where they train.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".