Gender disparity among psychiatry departments awarded Canadian Institutes of Health Research grants: a retrospective study
Bibliographic record
Abstract
Objective Although medical institutions aim to promote equity within the workplace, gender disparities persist in academic psychiatry. Previous evidence indicates that women in psychiatry encounter gender-based barriers to career advancement, resulting in slower rates of promotion, lower research productivity and less grant funding than men. Here, we investigate gender disparity in Canadian Institutes of Health Research (CIHR) grant funding decisions for researchers from Canadian Departments of Psychiatry. Method Data since inception from the CIHR funding decision database were searched for awards to applicants affiliated with Canadian psychiatry departments. For each grant, we collected the principal investigator’s (PI) name, conferral year, duration and total funding contribution. PI gender was extracted from an agreement between self-reported gender identity on provincial or territorial physician directories, from an official institutional website biography, and a validated gender application programming interface. Primary analysis was conducted for all recipients from Canadian psychiatry departments (including physician and non-physician scientists), and secondarily within a subgroup of physician scientists alone. Results Women (both physician and non-physician scientists) consistently received fewer grants (40.75%) and were less likely to obtain multiple awards in a year than men. Most strikingly, women received a total of US$110 658 191 while men received over double this amount, totalling US$253 339 865. Women (both physician and non-physician scientists) also received shorter award durations (p=2.312e-06, rg=0.179), fewer awards per year (p=0.002128, rg=0.662) and less money per grant (p=1.583e-07, rg=0.205). Within the subgroup of physician scientists, women were awarded a total of US$22 901 569 altogether, while men received a total of US$144 451 178. Women also received significantly fewer grants per year than men (p=3.565e-05, rg=0.889). Conclusions Gender disparity in CIHR funding decisions may pose another barrier to career progression for psychiatrists who are women. Further work is recommended to reduce gender funding gap in medical academia.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".