Emergency department experiences of people who use drugs who left or were discharged from hospital against medical advice
Bibliographic record
Abstract
BACKGROUND: People who use drugs (PWUD) frequent emergency departments at a higher rate than the general population, and experience a greater frequency of soft tissue infections, pneumonia, and chronic conditions such as, HIV/AIDs and hepatitis C. This population has distinct health care considerations (e.g. withdrawal management) and are also more likely to leave or be discharged from hospital against medical advice. METHODS: This study examines the experiences of PWUD who have left or been discharged from hospital against medical advice to understand the structural vulnerabilities that shape experiences with emergency departments. Semi-structured qualitative interviews were conducted with 30 PWUD who have left or been discharged from hospital against medical advice within the past two years as part of a larger study on hospital care and drug use in Vancouver, Canada. RESULTS: Findings characterize the experiences and perceptions of PWUD in emergency department settings, and include: (1) stigmatization of PWUD and compounding experiences of discrimination; (2) perceptions of overall neglect; (3) inadequate pain and withdrawal management; and (4) leaving ED against medical advice and a lack of willingness to engage in future care. CONCLUSIONS: Structural vulnerabilities in ED can negatively impact the care received among PWUD. Findings demonstrate the need to consider how structural factors impact care for PWUD and to leverage existing infrastructure to incorporate harm reduction and a structural competency focused care. Findings also point to the need to consider how withdrawal and pain are managed in emergency department settings.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".