Advancing patient‐centered care for structurally vulnerable drug‐using populations: a qualitative study of the perspectives of people who use drugs regarding the potential integration of harm reduction interventions into hospitals
Bibliographic record
Abstract
AIMS: To explore the perspectives of structurally vulnerable people who use drugs (PWUD) regarding: (1) the potential integration of harm reduction interventions (e.g. supervised drug consumption services, opioid-assisted treatment) into hospitals; and (2) the implications of these interventions for patient-centered care, hospital outcomes and drug-related risks and harms. DESIGN: Semi-structured qualitative interviews. SETTING: Vancouver, Canada. PARTICIPANTS: Thirty structurally vulnerable PWUD who had been discharged from hospital against medical advice within the past 2 years, and hospitalized multiple times over the past 5 years. MEASUREMENTS: Semi-structured interview guide including questions to elicit perspectives on hospital-based harm reduction interventions. FINDINGS: Participant accounts highlighted that hospital-based harm reduction interventions would promote patient-centered care by: (1) prioritizing hospital care access and risk reduction over the enforcement of abstinence-based drug policies; (2) increasing responsiveness to subjective health needs (e.g. pain and withdrawal symptoms); and (3) fostering 'culturally safe' care. CONCLUSIONS: Hospital-based harm reduction interventions for people who use drugs, such as supervised drug consumption services and opioid-assisted treatment, can potentially improve hospital care retention, promote patient-centered care and reduce adverse health outcomes among people who use drugs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".