Suicide in the absence of mental disorder? A review of psychological autopsy studies across countries
Bibliographic record
Abstract
BACKGROUND: While numerous past reviews of psychological autopsy (PA) studies have examined the relationship between mental disorder and suicide, there has been little systematic investigation of suicide occurring in the absence of any identifiable psychiatric condition. AIM: This article reviews available literature on the topic by considering Axis I, sub-threshold, mild disorders and personality disorders. METHOD: We conducted a systematic review of PA studies from 2000 onwards. Studies included in the review had to clearly describe the proportion of suicide cases without a classifiable mental disorder or sub-threshold condition. RESULTS: Up to 66.7% of suicide cases remained without diagnosis in those studies that only examined Axis I disorders (n = 14). Approximately 37.1% of suicide cases had no psychiatric condition in research papers that assessed personality and Axis I disorders (n = 9), and 37% of suicides had no Axis I, sub-threshold/mild conditions (n = 6). In general, areas in China and India had a higher proportion of suicides without a diagnosis than studies based in Europe, North America or Canada. CONCLUSION: Variation in the proportion of suicide cases without a psychiatric condition may reflect cultural specificities in the conceptualization and diagnosis of mental disorder, as well as methodological and design-related differences between studies.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".