Emergency department care experiences among 2SLGBTQQIA+ patients: a mixed methods study
Bibliographic record
Abstract
BACKGROUND: Equity-deserving groups (EDG), including those who identify as two-spirit, lesbian, gay, bisexual, transgender, queer, questioning, intersex, and/or asexual (2SLGBTQQIA+), are disproportionately treated in the Emergency Department (ED). This study aimed to understand ED care experiences of 2SLGBTQQIA+ individuals compared to those who do not identify with an equity-deserving group in Kingston, Canada, ultimately aiming to enhance inclusivity and better meet healthcare needs. METHODS: Data were collected through a mixed qualitative/quantitative cross-sectional study using a novel electronic survey tool (Spryng.io), which purposely integrates qualitative and quantitative data, while minimising researcher bias. A community-based participatory approach was employed to involve community stakeholders. Participants were recruited from the Kingston Health Sciences Centre's ED, Urgent Care Centre, and at community-based organisations. Quantitative data were analysed using chi-squared tests, while qualitative data underwent thematic analysis. Results were triangulated. Focus group discussions with community partners were then undertaken to contextualise findings. RESULTS: Compared to persons who did not identify as belonging to an EDG (n = 949), 2SLGBTQQIA+ individuals (n = 118) felt their identity had a more negative impact on their care (p < 0.0001) and experienced more judgment and disrespect from healthcare providers (HCPs) (p < 0.0001). Four themes emerged from triangulation of qualitative and quantitative data: (1) mixed emotions regarding ED care; (2) transgender and non-binary health care considerations; (3) unmet mental health needs; and (4) lack of patient-centred care for 2SLGBTQQIA+ patients. CONCLUSIONS: 2SLGBTQQIA+ individuals often face unmet mental health care needs, requiring tailored mental health care provision in the ED. Intersectionality within the 2SLGBTQQIA+ population underscores the importance of trauma-informed care. Strategies to improve 2SLGBTQQIA+ healthcare include implementing safer spaces, clear feedback mechanisms, referrals to gender-affirming specialists, and privacy in triage. Further research should assess the impact of educational interventions on HCP knowledge and patient experiences in the ED.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".