Does risk-taking or alcohol misuse mediate the association between anger and suicidal ideation in male depression?
Bibliographic record
Abstract
Anger is among the core symptoms in male-specific inventories of depression and has consistently been linked with suicidal ideation. In this study, we assessed whether this link may be mediated via other prominent symptoms of depression in men, namely risk-taking and alcohol misuse. We used self-reported data from 322 men responding to a 3-wave survey over 6 months. Regression with mediation analysis was employed to test whether anger at baseline predicted suicidal ideation six months later through the mediating effects of risk-taking or alcohol misuse at 3 months. We found a statistically significant indirect effect (indicating a mediation effect) of anger at baseline on suicidality at 6-months follow-up through risk taking at 3-months follow-up (effect = 0.007, SE = 0.003, 99% Confidence interval = 0.0002 to 0.0161). Anger at baseline was not significantly associated with alcohol misuse at 3-months follow-up (β = .062, t = 0.919, p = .358), thus nullifying alcohol misuse as a possible mediator between anger and suicidal ideation. In conclusion, the results of this study suggest that risk-taking, but not alcohol misuse, may be a mediator between anger and suicidal ideation in the context of male depression. If these results are replicated, assessing anger and risk-taking may inform monitoring of suicidality. Also, anger and risk-taking may be promising targets for treatment aimed at reducing the risk of suicide.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".