Cultural differences in the relations between expressive flexibility and life satisfaction over time
Bibliographic record
Abstract
Background: Expressive flexibility refers to the ability to assess situational demands and adjust one's emotion expressions via enhancement or suppression. It has been associated with lower levels of depressive and anxiety symptoms and greater social acceptance. These relationships, however, have not yet been examined across cultures-where prior research has found cultural differences in norms on emotion displays and their associations with mental health. This study examined expressive flexibility across three cultural groups and their associations with life satisfaction and depressive symptoms over time. Methods: 276 first-year college students (146 Asian American, 71 European Americans, and 62 Latinx Americans) completed two online surveys during the first (T1) and thirteenth week (T2) of the Fall 2020 academic semester. Results: Results revealed no significant cultural group differences in the ability to enhance or suppress emotions. However, we found a significant ethnicity x enhancement ability interaction in predicting T2 life satisfaction, controlling for T1 life satisfaction, age, gender, and emotion regulation frequency. Specifically, greater ability to enhance one's emotions was significantly associated with higher life satisfaction over time among Asian Americans, but not for European Americans and Latinx Americans. Discussion: Our findings illustrate the importance of not looking just at cultural group differences in the levels of expressive flexibility, but also at the associations between expressive flexibility and mental health.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".