Starting rural, staying rural: How can we strengthen the pathway from rural upbringing to rural practice?
Bibliographic record
Abstract
OBJECTIVE: To determine if selecting rural background students into the Monash Bachelor of Medicine and Bachelor of Surgery (MBBS) program affects vocational training location and intended practice location after training. DESIGN: Retrospective cohort mail survey. SETTING: Australia. PARTICIPANTS: Rural-background students at Monash 1992-1994 (n=24/40) and 1995-1999 (n=59/120) and urban background students (n=36/93 and 104/300, respectively). Overall study population: 62% female, average age of 28 years; 79% Australian-born; and 60% married/partnered. INTERVENTIONS: Rural or urban background, rural undergraduate exposure. MAIN OUTCOME MEASURES: Intent towards rural medical practice, vocational training location and subsequent practice location. RESULTS: There was a positive and significant (P ≤0.05) association between rural background and rural practice intent when respondents began (10-times higher than urban graduates) and completed (three times higher) their MBBS course. Rural practice intent increased fourfold in urban background graduates. There was a positive and significant association between rural background and preferred place of practice in 5-10 years in a Rural, Remote and Metropolitan Area (RRMA) 3-7 community (three times higher). There was a positive, but non-significant association between rural background and RRMA 3-7 community as their current location and first place of practice once vocationally qualified. CONCLUSIONS: Interest in rural practice is not fully reflected in location during or after vocational training. The beneficial effects of rural undergraduate exposure might be lost if internship and vocational training programs provide insufficient rural clinical experiences and curriculum content. Continuation of the rural pathway might be needed to maintain rural practice intent.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".