One-third of doctors completing specialist training in diabetes fail to secure a substantive consultant post: Young Diabetologists’ Forum survey 2010
Bibliographic record
Abstract
Reports have highlighted a shortage of consultant diabetologist posts in the UK. The number of doctors completing specialist training in diabetes has increased in recent years, but little is known about their employment after they receive their certificate of completion of training. An online survey was sent to all doctors who completed specialist diabetes training from January 2008 to September 2010. Of the 95 eligible respondents, 69 (73%) completed the survey (61% men; median age 36 years). Forty-three (62%) respondents secured substantive NHS consultant posts, and of those who gave their job breakdown, 48/51 (94%) were contributing to specialist diabetes care. Five (7%) respondents held substantive academic positions, while 11 (16%) were locum consultants. Seven (9%) respondents worked abroad, with half of these attributing their emigration to lack of opportunities in the UK. When asked about alternative choices, 39% of respondents were likely to seek 'general physician' roles, which equalled the number who would consider emigrating. Overall, only two-thirds of doctors who complete specialist training in diabetes secure substantive NHS consultant positions, which suggests a failure in workforce planning and a lack of expansion of the number of consultant posts despite progression of the diabetes epidemic.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".