The Relationship between Mood Instability and Suicidal Thoughts
Bibliographic record
Abstract
The objective of this study was to determine whether affective instability predicts suicidal thoughts. Data from a Dutch panel study (N = 1686) was used. Affective instability was assessed with 7 items representing suddenly shifting moods. Suicidal thoughts were assessed by the occurrence of suicidal thoughts in the past week. Negative affect was indexed by anxious, depressed and angry moods extracted by factor analysis. Odds ratios using logistic regression modeling were calculated, adjusting for clinical and demographic variables. The study found that both males (OR: 1.14; 95% CI: 1.02-1.28) and females (OR: 1.11; 95% CI: 1.00-1.23) were more likely to experience suicidal thinking with higher affective instability. Affective instability and negative affect independently predict suicidal thoughts. Affective instability requires more attention in the assessment 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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".