Short-stay crisis units for mental health service users on crisis care pathways: systematic review and meta-analysis
Bibliographic record
Abstract
Background \nInternationally, an increasing proportion of Emergency Department (ED) visits are mental health related. Concurrently, psychiatric wards are often occupied above capacity. Responding to these pressures, healthcare providers have introduced short-stay, hospital-based crisis units offering a therapeutic space for stabilisation, assessment and appropriate referral. Research lags behind roll-out, and a review of the evidence is urgently needed to inform policy and further introduction of similar units. \nAims \nThis systematic review aims to evaluate the effectiveness of short-stay, hospital-based mental health crisis units. \nMethod \nWe searched Embase, MEDLINE, CINAHL and PsycINFO up to March 2021 in this pre-registered review (PROSPERO: CRD42019151043). All designs incorporating a control or comparison group were eligible for inclusion, and all effect estimates with a comparison group were extracted and combined meta-analytically where appropriate. We assessed risk of bias of included studies using Risk Of Bias In Non-randomized Studies - of Interventions (ROBINS-I) and Risk of Bias in randomized trials (RoB 2). \nResults \nData from twelve studies from six countries (Australia, Belgium, Canada, the Netherlands, UK and US) and 67,505 participants were included. Data indicated that units delivered benefits on many outcomes. Units could reduce psychiatric holds (42% after intervention compared to 49.8% before intervention; difference = 7.8%; p < 0.0001) and increase outpatient follow-up care (χ2=37.42; d.f.=1, p<0.001). Meta-analysis indicated a significant reduction in length of ED stay of 164.24 minutes (95%CI -261.24 to -67.23 minutes; p<0.001), and number of inpatient admissions, odds ratio=0.55 (95% CI 0.43 to 0.68; p<0.001). \nConclusions \nShort-stay mental health crisis units are effective for two important service-defined outcomes; reducing ED wait times and inpatient admissions. Further research should investigate impact of units on patient experience, and clinical and social outcomes.
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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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".