Urban washout: How strong is the rural-background effect?
Bibliographic record
Abstract
OBJECTIVE: To test predictors of practice location of fully qualified Monash University Bachelor of Medicine, Bachelor of Surgery (MBBS) graduates. DESIGN: Cohort survey, 2011. SETTING: Australia. PARTICIPANTS: Rural (n = 67/129) and urban (n = 86/191) background doctors starting at Monash University 1992-1999. Approximately 60% female, 77% married/partnered, 79% Australian-born, mean age 34 years, 31% general practitioners, 72% fully qualified and 80% training/practising in major cities. MAIN OUTCOME MEASURES: First and current practice location once fully qualified. Intended practice location in 5-10 years. RESULTS: Logistic regression found that rural versus urban background was a significant predictor of rural (outside major city) first practice location (odds ratio (OR) 5.0, 95% confidence interval (CI) 1.3-19.2) and rural current practice location (OR 5.6, 95% CI 1.5-21.2) for fully qualified doctors. General practitioner versus other medical specialists significantly predicted first (OR 7.2, 95% CI 2.1-25.2) or current (OR 3.6, 95% CI 1.1-11.9) rural practice location. Preference for a rural practice location in 5-10 years was predicted by rural background (OR 4.4, 95% CI 1.6-11.8) and positive intention towards rural practice upon completing MBBS (OR 4.6, 95% CI 1.7-12.6). Surveyed in 2011, 28% of those who also responded to the 2006 survey shifted their preferred future practice location from rural to urban communities versus 13% shifting from urban to rural (McNemar-Bowker test, P = 0.02). CONCLUSION: The majority of fully qualified Monash MBBS graduates practicing in rural communities have rural backgrounds. The rural-background effect diminished over time and may need continued support during training and full practice.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".