Tuning medical education for rural-ready practice: designing and resourcing optimally
Bibliographic record
Abstract
In an effort to bring doctors back to the bush the Australian government has resourced a number of rural clinical schools (RCS). At the RCS in the University of Western Australia students were allocated in small groups to rural sites for the entire fifth year of a six-year course, sitting the same final examinations as city students. Key factors guiding the successful outcome were the resourcing and implementation of the infrastructure and teaching and learning pedagogy. In designing support, the disconnection of students from their city colleagues was anticipated as an issue, as was the pedagogical indoctrination of the teachers. The curriculum implementation was adapted in this light. The role of the Web in teaching and learning, and their status as 'student colleagues' and independent learners were pivotal aspects. As students settled at their site, their confidence grew and their anxiety over urban disconnection dissipated. By benchmarking themselves using Web-based formative assessments and in formative 'objective structured clinical examinations' staged for them by the RCS, the students received ongoing feedback on their progress. This model of embedding students in rural centres for an extended period with rural practitioners as teachers was successfully implemented at multiple sites geographically vastly separate.
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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 | 0.040 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".