Mission and role modelling in producing a fit‐for‐purpose rural health workforce: perspectives from an international community of practice
Bibliographic record
Abstract
In a perspective piece, the authors reflected on the implications of COVID-19 in terms of speeding up uptake of equity-promoting initiatives, such as distributed education and telehealth.• Interruptions to medical education during the COVID-19 pandemic have highlighted inequities across the health and health education systems, and prompted new and increased use of online learning (including for clinical skills), use of online examinations, and deployment of students to aid in the health response to COVID-19.• The COVID-19 pandemic presents an opportunity to disrupt conventional approaches to medical education and consider how necessary adaptations can drive change that is beneficial for medical education and health equity.• Lessons from THEnet partner schools provide guidance on successful innovations to achieve these aims.
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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 | 0.087 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.033 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.012 | 0.020 |
| 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".