Psychosocial interventions targeting suicidality within inpatient psychiatry: a scoping review
Bibliographic record
Abstract
BACKGROUND: The week following discharge from inpatient psychiatry is a time of exponentially increased risk of death by suicide among people diagnosed with a mental illness. Yet, a significant number of individuals do not receive timely outpatient follow-up upon being discharged from hospital. Psychosocial interventions that target suicidality initiated within inpatient psychiatry may help to prepare individuals for this vulnerable transition. AIMS: To explore the range and nature of research on psychosocial interventions targeting suicidality in the context of inpatient psychiatry and to identify the components and intended mechanisms of studied interventions as well as their mode and dose of delivery, adaptations, outcomes measured, and overall results. METHODS: This scoping review was conducted in accordance with the Joanna Briggs Institute (JBI) methodology for scoping reviews and in line with the Preferred Reporting for Systematic Reviews and Meta-Analyses extension for Scoping reviews. Studies that described psychosocial interventions initiated in adult psychiatric inpatient units aimed at addressing suicidality were included. RESULTS: A total of 77 articles were included in the review, most of which employed quantitative research methods, were published after 2019, and were conducted in the United States. Most interventions studied were based on cognitive behavioral approaches, incorporated a review of factors that led to a person's suicidality, strategies for managing emotions, and safety planning. Participants' narratives as to what was helpful for them centred around the themes of being acknowledged, being listened to, and increasing self-awareness. CONCLUSIONS: Cognitive behavioral therapy underlies most interventions targeting suicidality in inpatient psychiatry. There is a need for research that explores patients' experiences of engaging in specific suicide targeted interventions and that sheds light on the relational aspect of interventions targeting suicidality.
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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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".