Nature, costs and benefits of clinical travelling fellowships
Bibliographic record
Abstract
INTRODUCTION Many trainee doctors and consultants visit clinical units abroad for a period of specialised training. There are a number of grants available from professional and other bodies that provide a variable degree of financial assistance for these doctors but there is little information about the nature, costs and benefits of these training opportunities. METHODS This 11-year analysis of 385 applications to the Hospital Corporation of America International Foundation for financial support to train abroad was coupled with a detailed questionnaire to 127 UK doctors who received an award following an interview process. RESULTS There were an average of 11 annual awards, with a mean value of £5,600 (range: £1,000-£15,000). Trainees (predominantly ST7 and ST8 level) were the main applicants (60%) and award winners (71%). The applications were for variable time periods (from 1 month to over 24 months) and to clinical units throughout the world, the favoured locations being America, Canada and Australia. The surgical specialties were the most sought after for training (77%). There were 4.7 times more male than female applicants. CONCLUSIONS This paper discusses the benefits of travelling fellowships as recorded by grant recipients, three-quarters of whom were applying their overseas clinical experience back in the National Health Service. However, the overall costs of travel frequently exceed doctors' expectations and the need for extra financial support for overseas fellowships is clear.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".