Current status and solutions for gender equity in anaesthesia research
Bibliographic record
Abstract
Despite increasing numbers of women entering anaesthesia, they remain persistently under-represented within academic anaesthesia and research. Gender discordance is seen across multiple aspects of research, including authorship, editorship, peer review, grant receipt, speaking and leading. Women are also under-represented at higher faculty ranks and in department chair positions. These inequities are further magnified for women with intersectional identities, such as those who identify as Black, indigenous and women of colour. Several barriers to participation in research have been identified to date, including a disproportionate amount of family responsibilities, a disproportionate burden of clinical service, gender bias, sexual harassment and the gender pay gap. Several strategies to improve gender equity have been proposed. Increasing access to formal mentorship of women in academic medicine is frequently cited and has been used by healthcare institutions and medical societies. Senior faculty and leaders must also be conscious of including women in sponsorship and networking opportunities. Institutions should provide support for parents of all genders, including supportive parental leave policies and flexible work models. Women should also be materially supported to attend formal educational conferences targeted for women, aimed at improving networking, peer support and professional development. Finally, leaders must display a clear intolerance for sexual harassment and discrimination to drive culture change. Peers and leaders alike, of all genders, can act as upstanders and speak up on behalf of targets of discrimination, both in the moment or after the fact. Gender inequities have persisted for far too long and can no longer be ignored. Diversifying the anaesthesia research community is essential to the future of the field.
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 | 0.278 | 0.255 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.013 | 0.046 |
| Scholarly communication | 0.030 | 0.041 |
| Open science | 0.009 | 0.034 |
| Research integrity | 0.021 | 0.026 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".