“Maybe if I stop the drugs, then maybe they’d care?”—hospital care experiences of people who use drugs
Bibliographic record
Abstract
BACKGROUND: Drug use is associated with increased morbidity and mortality but people who use drugs experience significant barriers to care. Data are needed about the care experiences of people who use drugs to inform interventions and quality improvement initiatives. The objective of this study is to describe and characterize the experience of acute care for people who use drugs. METHODS: We conducted a qualitative descriptive study. We recruited people with a history of active drug use at the time of an admission to an acute care hospital, who were living with HIV or hepatitis C, in Toronto and Ottawa, Canada. Data were collected in 2014 and 2015 through semi-structured interviews, audio-recorded and transcribed, and analyzed thematically. RESULTS: Twenty-four adults (18 men, 6 women) participated. Participants predominantly recounted experiences of stigma and challenges accessing care. We present the identified themes in two overarching domains of interest: perceived effect of drug use on hospital care and impact of care experiences on future healthcare interactions. Participants described significant barriers to pain management, often resulting in inconsistent and inadequate pain management. They described various strategies to navigate access and receipt of healthcare from being "an easy patient" to self-advocacy. Negative experiences influenced their willingness to seek care, often resulting in delayed care seeking and targeting of certain hospitals. CONCLUSION: Drug use was experienced as a barrier at all stages of hospital care. Interventions to decrease stigma and improve our consistency and approach to pain management are necessary to improve the quality of care and care experiences of those who use drugs.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".