Self-report depressive symptoms do not directly predict suicidality in nonclinical individuals: Contributions toward a more psychosocial approach to suicide risk
Bibliographic record
Abstract
Although suicidality is associated with mental illness in general and depression in particular, many depressed individuals do not attempt suicide and some individuals who attempt to or do die by suicide do not present depressive symptoms. This article aims to contribute to a more psychosocial approach to understanding suicide risk in nonclinical populations. In advocating a psychosocial perspective rather than a depression-focused approach, this article presents four diverse studies that demonstrate sampling and measurement invariance in findings across different populations and specific measures. Study 1 tests the mediation effects of 2 interpersonal variables, thwarted belongingness and perceived burdensomeness, in the association between depressive symptoms and recent suicidality. Studies 2 and 3 evaluate the contribution of hopelessness and psychache, beyond depressive symptoms, to suicidality. Study 4 tests the contribution of life events behind depressive symptoms, and other relevant sociodemographic and clinical variables, to the estimation of "future suicidality." Overall, results demonstrate that depressive symptoms do not directly predict suicidality in nonclinical individuals, but that other psychosocial variables mediate the association between depressive symptoms and suicidality or predict suicidality when statistically controlling for depressive symptoms. The article contributes to understanding some of the nonpsychopathological factors that potentially link depressive symptoms to suicide risk and that might themselves contribute to suicidality, even when controlling for depressive symptoms.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".