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Record W4409329003 · doi:10.1016/j.ypmed.2025.108279

Effectiveness of suicide postvention service models and guidelines 2014–2024: A scoping review

2025· review· en· W4409329003 on OpenAlexaboutno aff
Chandra Ramamurthy, Trisnasari Fraser, Karolina Krysińska, Jacinta Hawgood, Kairi Kõlves, Lennart Reifels, Nicola Reavley, Karl Andriessen

Bibliographic record

VenuePreventive Medicine · 2025
Typereview
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
FundersTasmanian Department of HealthSax Institute
KeywordsMedicineService (business)Nursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.296
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.146
GPT teacher head0.487
Teacher spread0.341 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations6
Published2025
Admission routes1
Has abstractyes

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