Assessing and Documenting Gender Identity in Emergency Departments: A Narrative Literature Review
Bibliographic record
Abstract
Background: Many equity-deserving populations utilize the emergency department (ED) as they lack access to primary health care. Transgender and gender diverse (TGD) people are one of those populations. Despite improving rights for TGD people in Canada, transphobia, trans-hate, and cis-normativity are rising in the country. The literature shows that TGD people avoid the ED due to previous experiences of oppression within the health care system. The current state of science supports the need for health care providers (HCPs) to receive more education on the experiences of TGD patients. Methods: A narrative literature review was utilized to synthesize best practices for accurately assessing and documenting the gender identity (GI) of patients who present to the ED. Findings: The literature recommends that HCPs include GI in their documentation and electronic patient records; however, this frequently does not occur. Without acknowledging, affirming, and documenting patients' GI, ED HCPs perpetuate existing discrimination and miss key information by not accurately accounting for their TGD patients' identities. This poster will present the literature review’s implications (importance of documenting GI, how questions about GI are asked by HCPs, patient and HCP comfort with GI questions, and the preferred method of asking GI questions) and recommendations (a two-step questionnaire that asks about sex assigned at birth and gender identity). Conclusion: HCPs must work to improve equity for their patients, especially for TGD patients. To work towards addressing health inequities, the medical community must first accurately acknowledge and affirm diverse identities. The first step is a two-step questionnaire.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".