Effectiveness of suicide postvention service models and guidelines 2014–2024: A scoping review
Bibliographic record
Abstract
OBJECTIVE: Effective suicide postvention services provide immediate and ongoing support for suicide loss survivors. This review synthesizes peer-reviewed and grey literature exploring which suicide postvention service models have demonstrated effectiveness in reducing distress and supporting recovery in families, friends, and communities impacted by suicide. METHODS: The scoping review adhered to the updated Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). We conducted searches in five databases which included MEDLINE, PsycINFO, Embase, EBM Reviews, and Web of Science for peer reviewed studies and through Google search for grey literature. RESULTS: We identified 19 peer-reviewed studies and 14 guidelines (2014-2024) from the US, Canada, Australia, New Zealand, and Europe, which varied in measures, settings, and populations but lacked quality and generalizability. Guidelines based on theoretical models, particularly the public health model, aligned postvention with addressing the diverse needs of suicide loss survivors. CONCLUSIONS: The review identified potentially effective postvention components, such as the use of trained volunteers in support and therapy groups, workplace training programs and arts-based interventions, which could benefit those bereaved by suicide in Australia.
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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 | 0.030 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".