The distribution of optimism across sociodemographic groups in 22 countries
Bibliographic record
Abstract
Prior research (mainly from Western industrialized countries) documents associations between greater dispositional optimism (a generalized expectation that good things will happen) and improved health and well-being. However, less is known about whether and how levels of optimism differ across countries and across sociodemographic groups within different countries. This study presents a cross-national exploration of optimism, and its variations across sociodemographic groups. Using a sample of 202,898 adults from 22 diverse countries, we examined the relationships between optimism and key sociodemographic factors in each country separately, and cross-nationally by pooling results across countries using meta-analytic techniques. Our results suggest that mean optimism levels vary substantially across countries. Optimism also varies significantly across most of the sociodemographic factors included in our analyses. In the pooled results across countries, individuals who are older, female, married, employed, highly educated, attending religious services frequently, and native-born reported higher mean optimism levels. In the country-specific analyses, the sociodemographic variation in optimism differs across countries, indicating diverse societal influences. The findings of this study provide novel insights into the population distribution of optimism and disparities in optimism by sociodemographic groups across countries. This study provides a valuable foundation for future investigations into sociocultural influences that shape optimism.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".