Factors influencing the length of stay in forensic psychiatric settings: a systematic review
Bibliographic record
Abstract
BACKGROUND: Forensic psychiatry is often associated with long admissions and has a high cost of care. There is little known about factors influencing length of stay (LOS), and no previous systematic review has synthesised the available data. This paper aims to identify factors influencing the LOS in forensic psychiatry hospitals to inform care and interventions that may reduce the length of admissions. METHODOLOGY: A systematic review was conducted by searching major databases, including PubMed, EMBASE and PsycInfo, from inception until May 2022. Observational studies conducted in forensic hospitals that examined associations between variables of interest and LOS were included. Following data extraction, the Newcastle‒Ottawa Scale was used for quality appraisal. No meta-analysis was conducted due to heterogeneity of information; a quantitative measure to assess the strength of evidence was developed and reported. RESULTS: A total of 28 studies met the inclusion criteria out of 1606 citations. A detailed quantitative synthesis was performed using robust criteria. Having committed homicide/attempted homicide, a criminal legal status with restrictions, and a diagnosis of schizophrenia-spectrum disorders were all associated with longer LOS. Higher Global Assessment of Functioning (GAF) scores were associated with a shorter LOS. CONCLUSION: High-quality research examining factors associated with LOS in forensic psychiatry is lacking, and studies are heterogeneous. No modifiable characteristics were identified, and thus, practice recommendations were not made. There is an increasing necessity to understand the factors associated with longer admissions to inform care and increase success in reintegration and rehabilitation. This paper provides recommendations for future research.
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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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".