Opportunities to improve inpatient services and reduce rates of patient-direct discharge among people who use substances
Bibliographic record
Abstract
PURPOSE: Patients who use substances (PWUS) report experiencing stigmatizing encounters and undertreatment of pain and withdrawal symptoms that increase the likelihood of patient-directed discharge (PDD). This scoping review examines North American literature to gain insights about how institutional factors intersect with patient experiences and contribute to PDD. METHODS: A scoping review was conducted using MEDLINE, CINAHL, Scopus, and EMBASE databases. Screening was completed by two reviewers. A data extraction tool developed by the research team was used to collect demographic information and explore patients' experiences and reasons for PDD. RESULTS: We present four themes related to PDD: i) effective management of pain and withdrawal symptoms, ii) therapeutic alliance with healthcare providers, iii) hospital policies, protocols, and procedures, and iv) recommendations. Notably, all patients in all qualitative studies reported predominant experiences of uncaring, stigmatizing interactions with healthcare providers. DISCUSSION: Findings suggest that transformations are required at individual and institutional levels. At an individual level, to provide equitable care to all patients, healthcare providers in all practice settings should be competent to effectively and compassionately care for PWUS. At an institutional level, policies need to be re-envisioned to support the implementation of effective practices. CONCLUSION: Hospitals are faced with the challenges to ensure respectful care environments guided by harm reduction policies that will improve engagement of PWUS in services.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".