Perceiving societal pressure to be happy is linked to poor well-being, especially in happy nations
Bibliographic record
Abstract
Abstract Happiness is a valuable experience, and societies want their citizens to be happy. Although this societal commitment seems laudable, overly emphasizing positivity (versus negativity) may create an unattainable emotion norm that ironically compromises individual well-being. In this multi-national study (40 countries; 7443 participants), we investigate how societal pressure to be happy and not sad predicts emotional, cognitive and clinical indicators of well-being around the world, and examine how these relations differ as a function of countries' national happiness levels (collected from the World Happiness Report). Although detrimental well-being associations manifest for an average country, the strength of these relations varies across countries. People's felt societal pressure to be happy and not sad is particularly linked to poor well-being in countries with a higher World Happiness Index. Although the cross-sectional nature of our work prohibits causal conclusions, our findings highlight the correlational link between social emotion valuation and individual well-being, and suggest that high national happiness levels may have downsides for some.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".