A Systematic Mapping of Suicide Bereavement and Postvention Research and a Proposed Strategic Research Agenda
Bibliographic record
Abstract
BACKGROUND: Suicide may have disruptive and/or devastating effects on family, friends, and the broader community. Of late, increased interest from suicide researchers has given rise to an upsurge in research productivity addressing suicide bereavement and postvention. At this critical juncture, the establishment of an agenda will help guide the direction of future scholarly research in this field. AIMS: To conduct an exhaustive systematic mapping review and bibliometric analysis of peer-reviewed suicide bereavement and postvention research published over the past 50 years. METHOD: A comprehensive and strategic search of electronic databases and web-based search engines for original research studies was conducted resulting in the identification of 443 articles. RESULTS: Since 1965, the global research activities in the field of suicide bereavement and postvention is approximately 8.86 papers per year. There remains a lack of evaluation studies on the effects of interventions/programs with the majority of papers being explanatory in nature. Several areas of study within this field remain neglected. LIMITATIONS: While the search strategy was rigorous, potential limitations exist due to nonstandardized nomenclature and English language only inclusion, which inherently favors research from high-income countries. CONCLUSION: Suggested topics for a research agenda are proposed from the current limitations in the field.
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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 | 0.075 | 0.102 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.039 | 0.031 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.007 | 0.005 |
| 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".