Mood instability and impulsivity as trait predictors of suicidal thoughts
Bibliographic record
Abstract
OBJECTIVES: Impulsivity, the tendency to act quickly without adequate planning or concern for consequences, is a commonly cited risk factor for suicidal thoughts and behaviour. There are many definitions of impulsivity and how it relates to suicidality is not well understood. Mood instability, which describes frequent fluctuations of mood over time, is a concept related to impulsivity that may help explain this relationship. The purpose of this study was to determine whether impulsivity could predict suicidal thoughts after controlling for mood instability. METHODS: This study utilized longitudinal data from the 2000 Adult Psychiatric Morbidity Survey (N = 2,406). There was a time interval of 18 months between the two waves of the study. Trait impulsivity and mood instability were measured with the Structured Clinical Interview for DSM-IV Axis II Personality Disorders. Logistic regression analyses were used to evaluate baseline impulsivity and mood instability as predictors of future suicidal thoughts. RESULTS: Impulsivity significantly predicted the presence of suicidal thoughts, but this effect became non-significant with mood instability included in the same model. CONCLUSIONS: Impulsivity may be a redundant concept when predicting future suicidal thoughts if mood instability is considered. The significance is that research and therapy focusing on mood instability along with impulsivity may be useful in treating the suicidal patient. PRACTITIONER POINTS: Mood instability and impulsivity both predict future suicidal thoughts. Impulsivity does not predict suicidal thoughts after controlling for mood instability. Assessing and treating mood instability could be important aspects of suicide prevention and risk management.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".