Managing substance abuse on psychiatric units: a scoping review
Bibliographic record
Abstract
Objective: Substance use during psychiatric hospitalization compromises safety, treatment engagement, and post-discharge outcomes, but practical guidance for ward staff remains limited. This scoping review mapped the peer-reviewed literature on how psychiatric inpatient units detect, manage, and respond to alcohol or drug use that occurs on the ward itself, and summarized the effectiveness and breadth of reported strategies. Methods: The review followed the PRISMA-ScR framework. PubMed, Embase, PsycINFO, and Google Scholar were searched from inception to April 2025 using controlled vocabulary and free-text terms for substance use, psychiatric inpatients, and management strategies. English- and French-language empirical studies, quality-improvement reports, policy evaluations, and scoping reviews were eligible if they described an intervention or protocol applied in an inpatient psychiatric setting. Reviewers independently screened titles/abstracts and full texts extracted data with a standardized spreadsheet, and applied Joanna Briggs Institute critical-appraisal tools. Results: From the identified studies, 18 studies met inclusion criteria: 1 randomized trial, 3 quasi-experimental reports, 8 descriptive prevalence/cross-sectional studies, 2 policy case studies, 3 reviews/chapters, and 1 commentary. Seven recurring intervention domains were identified: systematic screening (n = 9 studies), brief motivational interventions, policy / protocol development, environmental or security measures, harm reduction strategies, staff training and culture change, and discharge planning. Structured screening improved detection rates up to two-fold; brief interventions such as SBIRT and BIMI increased post-discharge treatment engagement and reduced 30-day readmissions by up to 18%. Zero-tolerance security measures showed inconsistent effects on contraband entry or aggression. Overall methodological quality was moderate, with most evidence derived from single-site implementations. Conclusions: Existing evidence suggests that standardized screening, ward-adapted brief interventions, clear patient-centered policies, and targeted harm-reduction measures can feasibly improve management of inpatient substance use, while purely punitive security tactics are insufficient. Research gaps include rigorous multi-site evaluations, adolescent and forensic settings, and integrated harm-reduction protocols for substances other than nicotine.
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 | 0.025 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".