Impact of Suicide on Fellow Patients Exposed to Suicide in Clinical Settings and Postvention Strategies: A Scoping Review
Bibliographic record
Abstract
Objective Suicide in a clinical setting can cause trauma and grief, and affect other inpatients. There are different studies related to the bereavement after the suicide. This study aims to review the existing literature on the impact of suicide on fellow patients and the recommended interventions for them after the suicide in clinical settings. Materials & Methods This scoping review was conducted using the Joanna Briggs Institute framework. A medical librarian performed searches for related articles in English published from 2000 to 2020 in MedLine, Embase, APA PsycInfo, CINAHL, Web of Science, and Cochrane Library, and for the grey literature using Google and Google Scholar engines. The studies in non-clinical settings were excluded. Results The search yielded 873 records. Eleven articles and five guidelines met the inclusion criteria. The factors affecting the degree of impact on fellow patients were: Type of the relationship with the deceased, level of exposure to the suicide, the nature of suicide, and the patient’s vulnerability level. Immediate postvention recommendations emphasized the management of suicide risk and establishing safety, while short-term postvention recommendations aimed at providing emotional support and the recovery of fellow patients. Conclusion There is lack of studies on the impact of suicide in clinical settings on fellow patients and the postvention interventions for them. Further studies are needed to determine effective interventions for supporting this high-risk group.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 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".