Gender disparity in hepatobiliary endoscopy training and delivery: Results of a nationwide survey
Bibliographic record
Abstract
Background and study aims: There are far fewer female, independent endoscopic retrograde cholangiopancreatography (ERCP) practitioners than men in the UK. This study aimed to explore what lies behind the disparity by examining the current state of training, attitudes, and other factors that may influence trainees' decision to pursue ERCP training, with a view to identifying modifiable factors. Methods: Anonymized responses to an electronic survey distributed to gastroenterology trainees and independent ERCP practitioners in the UK were collected and analyzed. Results: Of 214 respondents 45% were female. Whereas gender distribution in non-hepatobiliary therapeutic endoscopy was balanced, only 29% of ERCP trainees were female. Eighty percent of those who worked less than full time (LTFT) were female, but 32% felt that LTFT was incompatible with ERCP training. Concerningly, one-quarter of female respondents reported that they had been discouraged. It was noted that females are often treated differently within endoscopy units, including by patients. Fifty percent of females indicated that radiation exposure affected their decision to train in ERCP, compared with 22% of males. A question specific to trainers revealed that 95% felt that male gender was associated with increased confidence. In free-text responses, valuable insights into trainees' personal experiences were provided, and a selection is presented. Finally, strategies to redress the gender imbalance are proposed. Conclusions: Gender disparity in HPB endoscopy exists and is stark. Underlying this are attitudes, assumptions, and environmental factors that will require systemic and sustained correction. Ideas about how to address this challenge need to be explored.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".