Characterizing the childhood roots of adult sense of mastery across 22 countries in the global flourishing study
Bibliographic record
Abstract
How might we cultivate a life imbued with a sense of mastery? An expanding body of research demonstrates that a heightened sense of mastery improves health and well-being outcomes. Despite this, it remains unclear which childhood factors foster increased mastery in adulthood. Further, existing studies have examined this question only within single countries. We analyzed nationally representative data from 22 countries in the Global Flourishing Study (N = 202,898) and evaluated if 11 aspects of a child's upbringing predict mastery in adulthood, and also whether these associations vary by country. Some childhood factors were associated with increased mastery in adulthood, including good health, good relationships with mothers and fathers, economic stability, and regular religious service attendance. Childhood factors associated with decreased mastery in adulthood included abuse, feeling like an outsider in one's family, poor health, economic hardship, and being female. However, there was little evidence that parent marital status or immigration status in childhood were associated with mastery in adulthood. Our meta-analysis also revealed substantial heterogeneity in childhood pathways to adult mastery across 22 countries. With further research, these findings could inform the development of globally adaptable, yet locally nuanced, programs and policies designed to foster a mastery across the globe.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".