Equity, diversity and inclusion in neurosurgery: results of the SBNS engagement survey
Bibliographic record
Abstract
INTRODUCTION Bullying, undermining and harassment (BUH) as well as discrimination have shown to be present throughout the National Health Service, especially in surgery. The Society of British Neurological Surgeons (SBNS) created its equity, diversity and inclusion (EDI) group to investigate the demographics of UK and Irish neurosurgeons, along with their experiences of BUH. METHODS A 31-item electronic survey was distributed through the national society email list for all neurosurgeons throughout the UK and Ireland in spring 2022. Descriptive statistics were used to present the results, along with 2x2 chi-squared contingency tables, with a p-value of <0.05 deemed statistically significant. RESULTS There were 175 respondents (approximately 18% of neurosurgeons in the UK and Ireland). Two-thirds (67%) were consultants and a quarter (23%) were women. Almost half (44%) were White British/Irish. A third (30%) went to university outside the UK/Ireland although most (82%) completed neurosurgical training in the UK or Ireland. Thirty-eight per cent said they did not have a primary caregiver or parental role. Two-thirds (65%) of respondents stated that they had been a victim of BUH, with 70% having witnessed BUH. Being female was the only demographic category with a significantly increased likelihood of experiencing BUH. A quarter (23%) of respondents felt uncomfortable about being open about themselves in the workplace and 39% had perceived a barrier to career progression due to a particular characteristic. CONCLUSIONS BUH and barriers to feeling comfortable in the workplace/career progression are problems in UK/Irish neurosurgery. Further work will be undertaken by the SBNS EDI working group to better understand the demographics of its community, to spread awareness of the issues, and to improve the experiences of neurosurgeons in the UK and Ireland.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".