Insight Into Mental Disorders and Suicidal Behavior
Bibliographic record
Abstract
OBJECTIVE: To investigate the association between insight into mental disorders and suicidal behavior. DATA SOURCES: English and French MEDLINE databases up to January 2014 were searched using suicide combined with awareness, consciousness, insight and anosognosia, unawareness, and awareness of illness. We also conducted a cross-sectional study comparing Mood Disorder Insight Scale (MDIS) and 24-item Hamilton Depression Rating Scale (HDRS-24), item 17, performance between 22 depressed (DSM-IV-TR criteria) suicide attempters and 22 patient controls. STUDY SELECTION: Study selection was based on the STROBE checklist. Selected studies were published in an English- or French-language peer-reviewed journal, included at least 1 measure of insight, and included patients with a history of suicidal behavior. Thirty-two studies were reviewed, of which 12 were longitudinal. DATA EXTRACTION: A review of the literature and meta-analysis of studies were conducted to compare insight in patients with versus those without a history of suicidal behavior. RESULTS: Most studies (25) were conducted in psychotic disorders. A small majority showed a positive association between 1 measure of insight and higher risk of suicidal ideas or acts in both psychotic and mood disorders. Our study found that suicide attempters, mostly female attempters, tended to have better insight into depression than patient controls according to the HDRS-24 (P = .06, effect size = 1.43 [95% CI, 0.77 to 2.09]) but not MDIS. Finally, a meta-analysis of 7 studies confirmed significantly better insight scores in suicide attempters, with a small effect size (Hedges g = -0.16 [95% CI, -0.3 to -0.03]). CONCLUSIONS: Overall, a significant but weak association was found between insight and the risk of suicidal behavior. We also raised methodological and conceptual concerns and discussed new measures (eg, test based).
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".