An Analysis of Two-Spirit, Lesbian, Gay, Bisexual, Transgender and Queer Research Funded by Canadian Institutes of Health Research
Bibliographic record
Abstract
Abstract Purpose Gender identity and sexual orientation are essential factors that must be incorporated into health research to ensure we unearth comprehensive and inclusive insights about the healthcare needs and experiences of diverse people. Despite the calls for more focus on sex and gender in health research, scant attention has been paid to gender identity or sexual orientation. Past research found that 0.35% of Canadian Institutes of Health Research (CIHR) grant abstracts mentioned studying lesbian, gay, bisexual, transgender, queer and/or Two-Spirit (2S/LGBTQ+)-specific health outcomes. However, the nature of that research was not explored. Methods Here we examine the publicly available database of grant abstracts funded by CIHR from 2009-2020 to analyze what type of 2S/LGBTQ+-specific health outcomes would be studied. Results We found that 58% of awarded grant abstracts mentioned studying sexually transmitted diseases, the majority of which were on human immunodeficiency virus (HIV). Less than 7% of funded 2S/LGBTQ+ grant abstracts mentioned studying cisgender women. Almost 40% mentioned including trans women/girls, and 30% mentioned including trans men/ boys. None of the studies examined mentioned work with the Two-Spirit community. Conclusion These results reflect larger social and health inequities that require structural level changes in research to support lesbian, bisexual and queer women’s health.
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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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.026 | 0.049 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".