P-914 - Can mood instability replace neurosis as an explanatory concept: a replication
Bibliographic record
Abstract
We recently reported (EPA 2011) that one factor of the Eysenck Neuroticism Scale (EPI-N) represents Mood Instability (MI) that was a significant predictor of suicidal thoughts. To increase our understanding of MI in psychological distress we examined a national sample with longitudinal follow-up: To determine whether factor analysis of the EPI-N scale yields a MI factor. To determine whether the MI factor predicts psychological well-being at follow-up. British Health and Lifestyle survey (1984) (N = 6,124) individuals that completed both of the following questionnaires. 3,232 individuals were followed in 1991–92. Eysenck Personality Inventory consists of 57 items that includes the EPI-N with 24 items. The General Health Questionnaire (GHQ) is a 30-item scale used to measure psychological distress in the community. The EPI-N was factor analyzed and the extracted factors were entered as predictors of GHQ (1991) in a linear regression model, while controlling for baseline (1984) GHQ score and important physical health and socio-demographic variables. We replicated the 3 factors of the EPI-N, the second factor represented MI. The other two factors represented mild symptoms of anxiety and depression. The 3 EPI-N factors (Including the MI factor), GHQ, hypertension and forced expiratory volume (1984) predicted GHQ (1991). Age, sex, marital status, occupational class, and household income were not predictors. MI is the salient and distinct feature of neuroticism. MI may be a more clinically and empirically useful concept than “neuroticism” and is a fertile subject for clinical and basic research.
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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 | 0.021 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.004 |
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".