Lesbian, gay, bisexual, transgender and or queer patient experiences in Canadian primary care and emergency departments: a literature review
Bibliographic record
Abstract
This literature review synthesises existing evidence and offers a thematic analysis of primary care and emergency department experiences of lesbian, gay, bisexual, transgender, queer and/or any other sexual or gender minority (LGBTQ+) individuals in Canada. Articles detailing first-person primary or emergency care experiences of LGBTQ + patients were included from EMBASE, MEDLINE, PsycINFO and CINHAL. Studies published before 2011, focused on the COVID-19 pandemic, unavailable in English, non-Canadian, specific to other healthcare settings, and/or only discussing healthcare provider experiences were excluded. Critical appraisal was performed following title/abstract screening and full-text review by three reviewers. Of sixteen articles, half were classified as general LGBTQ + experiences and half as trans-specific experiences. Three overarching themes were identified: discomfort/disclosure concerns, lack of positive space signalling, and lack of healthcare provider knowledge. Heteronormative assumptions were a key theme among general LGBTQ + experiences. Trans-specific themes included barriers to accessing care, the need for self-advocacy, care avoidance, and disrespectful communication. Only one study reported positive interactions. LGBTQ + patients continue to have negative experiences within Canadian primary and emergency care - at the provider level and due to system constraints. Increasing culturally competent care, healthcare provider knowledge, positive space signals, and decreasing barriers to care can improve LGBTQ + experiences.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.024 | 0.043 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".