EPA-1181 – Mood instability explains the relationship between impulsivity and depression
Bibliographic record
Abstract
Impulsivity is a frequently used explanation for acting without apparent forethought. Mood instability (MI) describes frequent repeated shifts in mood, often within a day. Both impulsivity and MI are related to depression. We used data from the 1984/1991 British Health and Lifestyle Survey to examine relationships between MI, impulsivity, and depression. Hypothesis: 1) both MI and impulsivity would be related to current and future depression, and 2) when MI was accounted for, impulsivity and depression would be unrelated. Latent variables representing MI and impulsivity were derived from the Eysenck Personality Inventory neuroticism and extraversion subscales respectively, and a depression latent variable was derived from the General Health Questionnaire, by using factor analysis. Structural equation modelling was used to determine if impulsivity was related to depression after MI was accounted for. Cross-sectionally, the correlation between impulsivity and depression (r = 0.22, p<0.001) became trivial in size (r = .05, p < .01) after the correlation between MI and impulsivity (r = 0.50, p<0.001) was removed from the structural model. Longitudinally, impulsivity no longer predicted future depression (β = −.04, p = .16) after MI was added to the structural model. These results indicate that any significant relationship between impulsivity and depression disappears when MI is considered. The implication is that research and therapy might be more productively directed at MI instead of impulsivity.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".