Emotion Regulation as a Moderator of the Ability Emotional Intelligence-Stress Reactivity Relationship
Bibliographic record
Abstract
Abstract: Ability emotional intelligence (AEI), defined as a set of skills specialized for processing emotional information, is associated with adaptive outcomes such as social support, well-being, and life satisfaction. However, recent research also suggests that higher AEI may result in higher stress reactivity. The present study evaluated both adaptive and maladaptive emotion regulation and forms of coping (task-, emotion-, and avoidance-oriented) as moderators of the AEI-stress reactivity relationship. We predicted that the relationship between AEI and stress reactivity would be moderated by adaptive and maladaptive emotion regulation and forms of coping. A total of 120 participants were recruited from a previous research study (aged 18–30, Mage = 20.69, SDage = 2.20, 46% women), whereby they completed measures of AEI, emotion regulation, and coping, followed by an in-person laboratory study one year later. Participants completed a pre-task assessment of their current emotional state and then presented a 3-min video-recorded presentation in front of two neutral evaluators. Galvanic skin response (GSR-M) was used to measure physiological stress. Results showed that maladaptive emotion regulation influenced the relationship between AEI (emotional understanding) and physiological stress, such that emotional understanding was significantly and positively related to physiological stress in participants with above-average levels of maladaptive emotion regulation. Our findings indicated that levels of AEI could have a deleterious effect on stress reactivity. That is, our findings suggest that when people low in EI tend to use suppression, the negative effects of maladaptive ER are amplified. Thus, emotion regulation strategies can be a critical factor in determining how much stress a person might experience when encountering an emotion-laden task.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".