Time for a Gut Check: A Qualitative Study of Proposed Interventions to Address Gender Inequality in Gastroenterology
Bibliographic record
Abstract
Background: Gender inequalities persist in medicine, particularly in some speciality fields where fewer women are employed. Although previous research has suggested potential interventions to broadly address gender inequality in medicine, no research has focused on interventions in the field of gastroenterology. The purpose of this research was to engage women in the field of gastroenterology in Canada, to identify interventions with potential to be effective in addressing gender inequality. Methods: A World Café was hosted in 2019 to discuss gender inequality and interventions in gastroenterology. Twelve women employed in the field of gastroenterology (i.e. physicians, nurses, research staff, and trainees) were purposively recruited and participated in the event. The discussion rounds were audio-recorded, transcribed, and thematic analyses was conducted using Braun and Clarke's principles. Results: Three key themes identifying potential interventions to address gender inequality in gastroenterology were generated: (1) Education; (2) Addressing institutional structures and polices; and 3) Role modelling and mentorship. Participants indicated that interventions should target various stakeholders, including both women and men in gastroenterology, young girls, patients, and administrators. Conclusion: Many of the interventions identified by participants correspond with existing research on interventions in general medicine, suggesting that institutional changes can be made for maximum effectiveness. Some novel interventions were also identified, including publicizing instances of gender parity and supporting interventions across the educational and professional lifecourse. Moving forward, institutions must assess their readiness for change and evaluate existing policies, programs, and practices for areas of improvement.
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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 | 0.032 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".