The Impact of Infectious Disease-Related Public Health Emergencies on Suicide, Suicidal Behavior, and Suicidal Thoughts
Bibliographic record
Abstract
Abstract. Background: Infectious disease-related public health emergencies (epidemics) may increase suicide risk, and high-quality evidence is needed to guide an international response. Aims: We investigated the potential impacts of epidemics on suicide-related outcomes. Method: We searched MEDLINE, EMBASE, PsycInfo, CINAHL, Scopus, Web of Science, PsyArXiv, medRxiv, and bioRxiv from inception to May 13–16, 2020. Inclusion criteria: primary studies, reviews, and meta-analyses; reporting the impact of epidemics; with a primary outcome of suicide, suicidal behavior, suicidal ideation, and/or self-harm. Exclusion criteria: not concerned with suicide-related outcomes; not suitable for data extraction. PROSPERO registration: #CRD42020187013. Results: Eight primary papers were included, examining the effects of five epidemics on suicide-related outcomes. There was evidence of increased suicide rates among older adults during SARS and in the year following the epidemic (possibly motivated by social disconnectedness, fears of virus infection, and concern about burdening others) and associations between SARS/Ebola exposure and increased suicide attempts. A preprint study reported associations between COVID-19 distress and past-month suicidal ideation. Limitations: Few studies have investigated the topic; these are of relatively low methodological quality. Conclusion: Findings support an association between previous epidemics and increased risk of suicide-related outcomes. Research is needed to investigate the impact of COVID-19 on suicide outcomes.
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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 | 0.014 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".