Hopelessness as a Predictor of Attempted Suicide among First Admission Patients with Psychosis: A 10‐year Cohort Study
Bibliographic record
Abstract
Little is known about the longitudinal relationship of hopelessness to attempted suicide in psychotic disorders. This study addresses this gap by assessing hopelessness and attempted suicide at multiple time-points over 10 years in a first-admission cohort with psychosis (n = 414). Approximately one in five participants attempted suicide during the 10-year follow-up, and those who attempted suicide scored significantly higher at baseline on the Beck Hopelessness Scale. In general, a given assessment of hopelessness (i.e., baseline, 6, 24, and 48 months) reliably predicted attempted suicide up to 4 to 6 years later, but not beyond. Structural equation modeling indicated that hopelessness prospectively predicted attempted suicide even when controlling for previous attempts. Notably, a cut-point of 3 or greater on the Beck Hopelessness Scale yielded sensitivity and specificity values similar to those found in nonpsychotic populations using a cut-point of 9. Results suggest that hopelessness in individuals with psychotic disorders confers information about suicide risk above and beyond history of attempted suicide. Moreover, in comparison with nonpsychotic populations, even relatively modest levels of hopelessness appear to confer risk for suicide in psychotic disorders.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".