Emergency department care experiences among people who use substances: a qualitative study
Bibliographic record
Abstract
BACKGROUND: People who use substances (PWUS) encounter significant barriers to accessing care for their complex health needs. As a result, emergency departments (EDs) often become the first point of healthcare access for many PWUS and are a crucial setting for the study of health inequities. This study aimed to understand the ED healthcare experiences of PWUS with the intent of informing ways of improving the delivery of equitable care. METHODS: This qualitative study was part of a larger cross-sectional, mixed-methods study that examined ED experiences among diverse underserved and equity-deserving groups (EDGs) within Kingston, Ontario, Canada. Participants shared and self-interpreted a story about a memorable ED or UCC visit within the preceding 24 months. Data from participants who self-identified as having substance use experiences was analyzed through inductive thematic analysis. RESULTS: Of the 1973 unique participants who completed the survey, 246 participants self-identified as PWUS and were included in the analysis. Most participants were < 45 years of age (61%), male (53%), and white/European (57%). 45% identified as a person with a disability and 39% frequently struggled to make ends meet. Themes were determined at the patient, provider, and system levels. PATIENT: history of substance use and experience of intersectionality negatively influenced participants' anticipation and perception of care. Provider: negative experiences were linked to assumption making, feelings of stigma and discrimination, and negative perceptions of provider care. Whereas positive experiences were linked to positive perceptions of provider care. System: timeliness of care and the perception of inadequate mental health resources negatively impacted participants' care experience. Overall, these themes shaped participants' trust of ED staff, their desire to seek care, and their perception of the care quality received. CONCLUSIONS: PWUS face significant challenges when seeking care in the ED. Given that EDs are a main site of healthcare utilization, there is an urgent need to better support staff in the ED to improve care experiences among PWUS. Based on the findings, three recommendations are proposed: (1) Integration of an equity-oriented approach into the ED, (2) Widespread training on substance use, and (3) Investment in expert resources and services to support PWUS.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".