Prevalence and forms of gender discrimination and sexual harassment among medical students and physicians in French-speaking Switzerland: a survey
Bibliographic record
Abstract
Objectives The aim of this study was to determine the prevalence and forms of gender discrimination and sexual harassment experienced by medical students and physicians in French-speaking part of Switzerland. Design and setting We conducted an online survey using a questionnaire of 9 multiple-choice and 2 open questions between 24 January 2019 and 24 February 2019. Our target population was medical students and physicians working at hospitals and general practitioners from the French-speaking part of Switzerland. The online survey was sent via social media platforms and direct emails. We compared answers between male-determined and female-determined respondents using either χ 2 or Fisher’s exact tests. Results Among 1071 responders, a total of 893 were included (625 females, 264 males, 4 non-binary and 1 non-binary and male). 178 were excluded because they did not mention their working place or were working only outside Switzerland. Because of the small number of non-binary participants, they were not contemplated in further statistical analysis. Of 889 participants left, 199 (31.8%) women and 18 (6.8%) men reported having personally experienced gender discrimination, in terms of sexism, difficulties in career development and psychological pressure. Among women, senior attendings were the most affected (55.2%), followed by residents (44.1%) and junior attendings (41.1%). Sexual harassment was equally observed among women (19.0%) and men (16.7%). Compared with men (47.0%), women (61.4%) expressed the need to promote equality and inclusivity in medicine more frequently (p<0.001), as well as the need for support in their professional development (38.7% women and 23.9% men; p<0.001). Conclusions Gender discrimination in medicine in French-speaking Switzerland affects one-third of women, in particular, those working in hospital settings and senior positions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".