Evaluating neurosurgical training: a national survey examining the British trainee experience
Bibliographic record
Abstract
PURPOSE: Neurosurgery training in the UK has undergone significant changes over the past few years, including the new competency-based curriculum and a reduction of elective operating due to the pandemic. We conducted a comprehensive survey to assess UK neurosurgical trainees' experiences and perceptions to develop targeted action plans. METHODS: An online anonymised survey was developed and distributed amongst the BNTA mailing list. Question types included 10-point Likert scales and free text options. Descriptive statistics, non-parametric testing of Likert scores, and Spearman's rank correlation were used to analyse responses. Pearson's chi-squared test was used for subgroup analysis of categorical data. RESULTS: A total of 75 trainees with a National Training Number (NTN) responded. Overall trainees feel they are well trained, well supported, and have caught up with training emerging out of COVID. Funding for training varied between deaneries. There is significant concern amongst trainees regarding the workforce crisis. This, as well as financial concerns are leading to more than a quarter of trainees considering quitting. Half of the trainees are considering going OOP. More than one third of the trainees and more than half of the female trainees are considering working Less Than Full Time (LTFT). Most important supportive mechanisms towards completion of training were social support, along with personal satisfaction from work. An independent mentoring scheme is a preferred additional support mechanism. CONCLUSIONS: Overall training experience for neurosurgery trainees in UK and Ireland was positive. There are significant concerns regarding the workforce crisis and costs of training, with a large proportion of neurosurgery trainees considering resigning. OOP and LTFT are popular means of becoming more competitive for consultant posts and to spend time with their families. Deanery experience, senior and peer support does, and will improve trainee experience and protect against attrition.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".