A comparative analysis of solitary suicides, suicides following homicide, and suicide pacts using the National Violent Death Reporting System
Bibliographic record
Abstract
BACKGROUND: Incidents of suicide can be categorized into three main types: solitary suicides, suicides following homicide, and suicide pacts. Although these three suicide incidents vary by definition, no studies to-date have simultaneously examined and compared them for potential differences. The objective of the current study was to empirically and descriptively compare solitary suicides, suicides following homicide, and suicide pacts in the United States. METHODS: Restricted-access data from the National Violent Death Report System for 2003-2019 for 262,679 solitary suicides, 4,352 suicides following homicide, and 450 suicide pacts were used. Pairwise comparisons of the three suicide incident types were made for demographic factors, method of suicide, preceding circumstances, mental health status, and toxicology findings. RESULTS: Solitary suicides, suicides following homicide, and suicide pacts have distinct profiles, with statistically significant (p < 0.05) differences across all pairwise comparisons of sex, race, ethnicity, marital status, education, method of suicide, financial problems, interpersonal relationship problems, physical health problems, mental health problems, mood disorders, suicide attempt history, and opiate use at the time of death. CONCLUSION: Despite sharing a few commonalities, solitary suicides, suicides following homicide, and suicide pacts represent distinct phenomena. Each of these suicide incident types likely have their own unique prevention pathways.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".