Including gender data in electronic health records to improve the care provided to transgender and non-binary patients in Quebec: a queer bioethics analysis
Bibliographic record
Abstract
Background: In 2011, the Institute of Medicine (IOM) released the first report to comprehensively compile the state of knowledge on LGBTQIA+ health at different stages of life. This report highlighted the need to include data on gender identity in electronic health records (EHRs), in addition to birth-assigned sex, to improve care for transgender or non-binary (TNB) patients and reduce health disparities. Quebec has yet to implement this change. Given the significant discrimination, marginalization and violence TNB patients face in healthcare, carefully examining the risks and potential benefits of this proposal through an ethical analysis is essential. This study aims to determine whether systematically including gender data in Quebec EHRs is ethically advisable. Methods: A critical interpretive literature review was conducted on three online databases (PubMed, ScienceDirect, and Google Scholar) to examine the proposition of gender data inclusion in EHRs. The findings were critically assessed through a queer bioethics lens. Results and discussion: We retrieved 24 articles that met our inclusion criteria. Four themes were identified and analyzed: 1) hierarchical binaries embedded in EHRs; 2) potential of increased stigmatization due to heightened visibility; 3) significance of gender-affirming clinical environments; and 4) clinical significance of documenting both birth-assigned sex and gender data. Conclusion: This analysis identifies key ethical considerations that must be addressed and ultimately finds that the benefits of including gender data in Quebec EHRs outweigh the risks. This change would represent a meaningful step toward a system that better affirms diverse identities, supports self-determination, and challenges normative frameworks in place.
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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 | 0.129 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.017 | 0.022 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".