EPA-1185 – The relationship between mood instability and suicidal thoughts
Bibliographic record
Abstract
Most people who commit suicide suffer from depression, but diagnostic categories and known personal and demographic factors do not adequately capture the distress that leads people to kill themselves. Mood Instability (MI) refers to sudden unpredictable fluctuations in mood often occurring within a day. To determine whether MI predicts suicidal thoughts. Hypothesis: That MI will predict suicidal thoughts even when negative affect (depression, anxiety, anger) are controlled. Data from the Dutch Imigrant panel of the Longitudinal Internet Studies for the Social Sciences (LISS-I) (N = 1686) were used. MI was assessed with 7 items from the International Personality Item Pool of Big Five Indicators. The Chronbach's alpha was 0.86. Suicidal thoughts were assessed by a single question referring to the past week. Depression, anxiety and anger were represented by 21 items derived by factor analysis from the 31-item Emotional Expressiveness Module. Odds ratios using logistic regression modeling were calculated, adjusting for negative affect, alcohol and substance abuse, and demographic variables (age, sex, income). MI predicts suicidal thoughts (Males OR: 1.14; 95% CI: 1.02-1.28 and females OR: 1.11; 95% CI: 1.00-1.23) along with negative affect. There was no interaction between MI and negative affect. MI has been relatively neglected as a predictor of suicidal thoughts. It is likely that the unpredictable, sudden, severe descents in mood are particularly distressing and contribute to the feeling that life is intolerable. MI requires more attention in the assessment and treatment of suicide risk.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".