Perceived Effectiveness of Components of Interventions to Support People Bereaved By Suicide
Bibliographic record
Abstract
Abstract: Background: Suicide bereavement increases the probability of adverse outcomes related to grief, social functioning, mental health, and suicidal behavior. While more support for individuals bereaved by suicide has become available, the evidence regarding its effectiveness is not straightforward. The literature suggests that identifying best-practice components is key in designing effective postvention interventions. Aims: This metareview aims to identify components of suicide bereavement interventions perceived to be effective by suicide-bereaved people. Method: The review adhered to preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Systematic searches in Medline, PsycINFO, Embase, Emcare, EBM Reviews, Scopus, and Web of Science identified 11 eligible systematic reviews published between 2008 and 2023. The methodological quality was assessed using the Measurement Tool to Assess Systematic Reviews (AMSTAR-2) (PROSPERO registration CRD42023458300). Results: Our narrative synthesis reported the components perceived to be effective in relation to structure and content of interventions, facilitators, and modality (peer, group, community, online). Limitations: The quality of the included reviews varied considerably, and not all reviews reported on perceived effectiveness of interventions’ components. Meta-analysis of findings was not possible due to study heterogeneity. Conclusion: The findings provide crucial information for researchers, service providers, and policymakers to enhance the provision of evidence-based support for people bereaved by suicide.
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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 | 0.019 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".