Perceptions and experiences of a gender gap at a Canadian research institute and potential strategies to mitigate this gap: a sequential mixed-methods study
Bibliographic record
Abstract
BACKGROUND: The gender gap in academia is long-standing. Failure to ensure that our academic faculty reflect our student pool and national population deprives Canada of talent. We explored the gender distribution and perceptions of the gender gap at a Canadian university-affiliated, hospital-based research institute. METHODS: We completed a sequential mixed-methods study. In phase 1, we used the research institute's registry of scientists (1999-2014) and estimated overall prevalence of a gender gap and the gap with respect to job description (e.g., associate v. full-time) and research discipline. In phase 2, we conducted qualitative interviews to provide context for phase 1 data. Both purposive and snowball sampling were used for recruitment. RESULTS: = 70) were clinicians; there were 54.2% more male than female clinician scientists. Ninety-five percent of basic scientists were men, indicating a 90.5% gap. Seven key themes emerged from 21 interviews, including perceived impact of the gender gap, factors perceived to influence the gap, recruitment trends, presence of institutional support, mentorship and suggestions to mitigate the gap. Several factors were postulated to contribute to the gender gap, including unconscious bias in hiring. INTERPRETATION: A substantial gender gap exists within this research institute. Participants identified strategies to address this gap, such as establishing transparent search processes, providing opportunities for informal networking and mentorship of female scientists and establishing institutional support for work-life balance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".