Suicide postvention for staff and students on university campuses: a scoping review
Bibliographic record
Abstract
OBJECTIVE: To examine current knowledge about suicide bereavement and postvention interventions for university staff and students. DESIGN: Scoping review. DATA SOURCES AND ELIGIBILITY: We conducted systematic searches in 12 electronic databases (PubMed, PsycINFO, MEDLINE, CINAHL, Africa-Wide Information, PsycARTICLES, Health Source: Nursing/Academic Edition, Academic Search Premier, SocINDEX through the EBSCOHOST platform; Cochrane Library, Web of Science, SCOPUS), hand searched lists of references of included articles and consulted with library experts during September 2021 and June 2022. Eligible studies were screened against the inclusion criteria independently by two reviewers. Only studies published in English were included. DATA EXTRACTION AND SYNTHESIS: Screening was conducted by two independent reviewers following a three-step article screening process. Biographical data and study characteristics were extracted using a data extraction form and synthesised. RESULTS: Our search strategy identified 7691 records from which 3170 abstracts were screened. We assessed 29 full texts and included 17 articles for the scoping review. All studies were from high-income countries (USA, Canada, UK). The review identified no postvention intervention studies on university campuses. Study designs were mostly descriptive quantitative or mixed methods. Data collection and sampling were heterogeneous. CONCLUSION: Staff and students require support measures due to the impact of suicide bereavement and the unique nature of the university context. There is a need for further research to move from descriptive studies to focus on intervention studies, particularly at universities in low-income and middle-income countries.
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 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 | 0.031 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".