Country of training and ethnic origin of UK doctors: database and survey studies
Bibliographic record
Abstract
OBJECTIVES: To report on the country of training and ethnicity of consultants in different specialties in the NHS, on trends in intake to UK medical schools by ethnicity, and on the specialty choices made by UK medical graduates in different ethnic groups. DESIGN: Analysis of official databases of consultants and of students accepted to study medicine; survey data about career choices made by newly qualified doctors. SETTING AND SUBJECTS: England and Wales (consultants), United Kingdom (students and newly qualified doctors). RESULTS: Of consultants appointed before 1992, 15% had trained abroad; of those appointed in 1992-2001, 24% had trained abroad. The percentage of consultants who had trained abroad and were non-white was significantly high, compared with their overall percentage among consultants, in geriatric medicine, genitourinary medicine, paediatrics, old age psychiatry, and learning disability. UK trained non-white doctors had specialty destinations similar to those of UK trained white doctors. The percentage of UK medical graduates who are non-white has increased substantially from about 2% in 1974 and will approach 30% by 2005. White men now comprise little more than a quarter of all UK medical students. White and non-white UK graduates make similar choices of specialty. CONCLUSIONS: Specialist medical practice in the NHS has been heavily dependent on doctors who have trained abroad, particularly in specialties where posts have been hard to fill. By contrast, UK trained doctors from ethnic minorities are not over-represented in the less popular specialties. Ethnic minorities are well represented in UK medical school intakes; and white men, but not white women, are now substantially under-represented.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".