Trait Emotional Intelligence, Anxiety Sensitivity, and Experiential Avoidance in Stress Reactivity and Their Improvement Through Psychological Methods
Bibliographic record
Abstract
Stress pervades daily society, often with deleterious consequences for those prone to react intensely to it. Intervention techniques to attenuate stress reactivity are thus paramount. With that goal in mind, researchers have sought to identify and alter malleable psychological dispositional variables that influence stress reactivity. Trait emotional intelligence (TEI), anxiety sensitivity (AS), and experiential avoidance (EA) are increasingly receiving attention in these research efforts. The self-reported emotional component of stress reactivity has been emphasized in investigations and is our focus. Specifically, this paper overviews the role of TEI, AS, and EA in self-reported stress responses. We also discuss empirically supported psychological methods to adjust suboptimal levels of these variables in normal populations. Both psycho-educational (information, skills) and mindfulness-based interventions (specific mindfulness therapies or components) are covered. Findings include that (1) TEI, AS, and EA are each correlated with the emotional component of stress reactivity to both naturalistic and lab-based stressors; (2) preliminary support currently exists for psycho-educational intervention of TEI and AS but is lacking for EA; (3) adequate evidence supports mindfulness-based interventions to target EA, with very limited but encouraging findings suggesting mindfulness methods improve TEI and AS; and (4) although more research is needed, stress management approaches based on mindfulness may well target all three of these psychological variables and thus appear particularly promising. Encouragingly, some methods to modify dispositional variables (e.g., a mindfulness-based format of guided self-help) are easily disseminated and potentially applicable to the general public.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 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".