Global evidence of gender equity in academic health research: a scoping review
Bibliographic record
Abstract
OBJECTIVES: To chart the global literature on gender equity in academic health research. DESIGN: Scoping review. PARTICIPANTS: Quantitative studies were eligible if they examined gender equity within academic institutions including health researchers. PRIMARY AND SECONDARY OUTCOME MEASURES: Outcomes related to equity across gender and other social identities in academia: (1) faculty workforce: representation of all genders in university/faculty departments, academic rank or position and salary; (2) service: teaching obligations and administrative/non-teaching activities; (3) recruitment and hiring data: number of applicants by gender, interviews and new hires for various rank; (4) promotion: opportunities for promotion and time to progress through academic ranks; (5) academic leadership: type of leadership positions, opportunities for leadership promotion or training, opportunities to supervise/mentor and support for leadership bids; (6) scholarly output or productivity: number/type of publications and presentations, position of authorship, number/value of grants or awards and intellectual property ownership; (7) contextual factors of universities; (8) infrastructure; (9) knowledge and technology translation activities; (10) availability of maternity/paternity/parental/family leave; (11) collaboration activities/opportunities for collaboration; (12) qualitative considerations: perceptions around promotion, finances and support. RESULTS: Literature search yielded 94 798 citations; 4753 full-text articles were screened, and 562 studies were included. Most studies originated from North America (462/562, 82.2%). Few studies (27/562, 4.8%) reported race and fewer reported sex/gender (which were used interchangeably in most studies) other than male/female (11/562, 2.0%). Only one study provided data on religion. No other PROGRESS-PLUS variables were reported. A total of 2996 outcomes were reported, with most studies examining academic output (371/562, 66.0%). CONCLUSIONS: Reviewed literature suggest a lack in analytic approaches that consider genders beyond the binary categories of man and woman, additional social identities (race, religion, social capital and disability) and an intersectionality lens examining the interconnection of multiple social identities in understanding discrimination and disadvantage. All of these are necessary to tailor strategies that promote gender equity. TRIAL REGISTRATION NUMBER: Open Science Framework: https://osf.io/8wk7e/.
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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 | 0.077 | 0.218 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.040 | 0.044 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| 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".