Economic data on interventions for reducing aggression and restrictive interventions in inpatient mental health: a systematic review
Bibliographic record
Abstract
OBJECTIVE: Given the importance of economic considerations for the uptake of interventions into psychiatric policy and practice, this systematic review appraises existing economic evaluations of interventions that aim to reduce aggression and restrictive interventions in inpatient mental health settings. METHODS: Eight economic and scientific databases, along with targeted Google Scholar searches, were surveyed for gray and peer-reviewed literature from 01/2000 to 08/2025. Selection criteria included: (1) quantitative studies in peer-reviewed journals or grey literature, (2) a broad range of economic evaluation methods (costs, cost analysis, cost-effectiveness, and cost-benefit), (3) non-geriatric adults and emerging adults (≥ 15 years old) in (4) psychiatric inpatient settings, and (5) non-pharmacological interventions targeting aggression, violence and/or restrictive interventions (e.g., seclusion, restraints, forced medication). Narrative synthesis is presented with a quality appraisal using the CHEERS reporting checklist 2022. RESULTS: Twenty studies were selected, with the majority conducted in acute wards. Eleven studies reported only the cost of interventions, prominently featuring the cost of restrictive interventions, sensory modulation, and staff training. Moreover, twelve studies reported savings, eight of which allowed cost-analyses. Two interventions yielded clinical benefits and net savings. Assessment of reporting quality revealed few sensitivity analyses to model uncertainty, heterogeneity or distributional effects. CONCLUSIONS: While this review intended to guide organizations in selecting interventions, the current state of evidence can provide some evidence on the cost-benefit of a handful of interventions and re-affirms the costliness of restrictive interventions. Future pre-post studies may benefit from methods featured in this review to estimate the cost of professional time and partner with organizations to access internal financial data. There remains a need for purposeful cost-effectiveness analyses and for demonstrated long-term clinical benefits to inform interventions for aggression management.
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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 | 0.025 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".