Predicting psychological health: assessing the incremental validity of emotional intelligence beyond personality, Type A behaviour, and daily hassles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although some research has linked emotional intelligence (EI) and psychological health, little research has examined EI's ability to predict health outcomes after controlling for related constructs, or EI's ability to moderate the stressor–strain relationship. The present study explored the relationships among EI (as assessed by a trait‐based measure, the EQ‐i), Big Five personality factors, Type A Behaviour Pattern (TABP), daily hassles, and psychological health/strain factors (in terms of perceived well‐being, strain, and three components of burnout). The EQ‐i was highly correlated with most aspects of personality and TABP. After controlling for the impact of hassles, personality, and TABP, the five EQ‐i subscales accounted for incremental variance in two of the five psychological health outcomes. However, the EQ‐i scales failed to moderate the hassles–strain relationship. Copyright © 2005 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 it