Role of Emotion Regulation Difficulties in Predicting Mental Health and W ell-being
Bibliographic record
Abstract
The present study reports the relationship of emotion regulation difficulties and alexithymia with mental health and subjective wellbeing of an individual. Two hundred and eighty eight participants (218 males and 70 females) in the age range of 16 to 38 years (Mean age =20.78, SD= 2.95 years) were assessed on a measure of alexithymia, difficulties in emotion regulation, general mental health, and subjective well-being (assessed by Positive Negative Affect Schedule and Satisfaction with Life Scale). The results of the bi-variate correlation analysis revealed that difficulties in understanding and communicating as well as regulating emotions, in general, have a negative influence on health and wellbeing. However, the findings of step-wise multiple regression analysis indicated that some specific types of emotional deficits such as difficulties in identifying feelings, lack of emotional clarity and limited access to emotion regulation strategies were relatively more important in predicting the health status and well-being of an individual as compared to other emotional difficulties. Overall the findings imply that emotion regulation difficulties and alexithymia in general is associated with impaired mental health and lower level of happiness and life satisfaction, i.e., subjective well-being. The observed findings have been discussed in the light of the available empirical evidences. The role of emotions and emotional experiences in determining the health status of an individual, though, has been focus of psychological inquiry since long the last few decades have witnessed an invigorated interest in this area (see review by Pandey and Choubey, 2010). While emotions can be adaptive in many ways, emotions can also be maladaptive (Amstadter, 2008). For example, researchers have noted that while positive emotional experiences and emotional intelligence have a positive effect on mental health, suppression of emotions, inability or difficultly in understanding and communicating emotions (e.g. alexithymia), disposition to exaggerate emotional experiences may have health impairing effect (Pandey and Choubey, 2010). The functional role of emotions in mental health and subjective well being has been highlighted by several researchers (Quoidbach et al, 2010). The manner in which individuals are able to manage their emotional experiences to confirm adaptively to a given context appears to be important in mental health (Gross 1995).,
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".