Effect of Door-Locking Policy on Inpatient Treatment of Substance Use and Dual Disorders
Bibliographic record
Abstract
OBJECTIVE: Substance use treatment is often performed inside locked wards. We investigate the effects of adopting a policy of open-door treatment for a substance use treatment and dual diagnosis ward. METHODS: This is a prospective open-label study investigating 3-month study periods before opening (P1), immediately after (P2), and 1 year after the first period (P3). Data on committed patients, coercion (seclusion, forced medication, absconding events with subsequent police search), violence, and substance use was collected daily. We applied generalised estimating equation models. RESULTS: The mean daily number of patients with ongoing commitment changed from 2.64 (P1) to 2.12 (P2) to 0.96 (P3), corresponding to a reduction of relative risk (RR) for having an ongoing commitment by 20% in P2 (RR 0.80; 95% CI 0.66-0.98) and 67% in P3 (RR 0.33; 95% CI 0.25-0.42). The mean daily number of coercive events was 0.29, 0.13, and 0.05, corresponding to a risk for undergoing coercive measures reduced by 56% (RR 0.44; 95% CI 0.22-0.90) and 85% (RR 0.15; 95% CI 0.05-0.45). Substance use, violence or ward atmosphere did not differ significantly. CONCLUSIONS: Our results support findings from general psychiatric wards of reduced coercion after adopting a primarily open-door policy. However, coercive events were rare during all periods. The widespread practice of restricting the freedom of inpatients with substance use disorders by locking ward doors is highly questionable.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".