Undergraduate teaching in UK general practice: a geographical snapshot
Bibliographic record
Abstract
BACKGROUND: Learning in general practice is an essential component of undergraduate medical education; currently, on average, 13% of clinical placements in the UK are in general practice. However, whether general practice can sustainably deliver more undergraduate placements is uncertain. AIM: To identify the geographical distribution of undergraduate teaching practices and their distance from the host medical school. DESIGN AND SETTING: National survey of all medical schools in the UK. METHOD: All 33 UK medical schools were invited to provide the postcodes of their undergraduate teaching practices. These were collated, de-duplicated, and mapped. The distance in kilometres and journey times by car and public transport between each medical school and its teaching practices was estimated using Transport Direct (www.transportdirect.info). The postcodes of every practice in the UK were obtained from the UK's health departments. RESULTS: All 33 UK medical schools responded; 4392 practices contributed to teaching, with a median (minimum-maximum) of 142 (17-385) practices per school. The median (minimum-maximum) distance between a school and a teaching practice was 28 km (0-1421 km), 41 (0:00-23:26) minutes' travel by car and 1 hour 12 (0:00-17:29) minutes' travel by public transport. All teaching practices were accessible by public transport in one school and 90-99% were in a further four schools; 24 schools had >20% of practices that were inaccessible by public transport. CONCLUSION: The 4392 undergraduate teaching general practices are widely distributed and potentially any practice, no matter how isolated, could contribute to undergraduate education. However, this is, at the price of a considerable travel burden.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".