Toward understanding the relationship between personality and well‐being states and traits
Bibliographic record
Abstract
OBJECTIVE: Although there is a robust connection between dispositional personality traits and well-being, relatively little research has comprehensively examined the ways in which all Big Five personality states are associated with short-term experiences of well-being within individuals. We address three central questions about the nature of the relationship between personality and well-being states: First, to what extent do personality and well-being states covary within individuals? Second, to what extent do personality and well-being states influence one another within individuals? Finally, are these within-person relationships moderated by dispositional personality traits and well-being? METHOD: Two experience sampling studies (N = 161 and N = 146) were conducted over 2 weeks. RESULTS: Across both studies, all Big Five personality states were correlated with short-term experiences of well-being within individuals. Individuals were more extraverted, emotionally stable, conscientious, agreeable, and open in moments when they experienced higher well-being (greater self-esteem, life satisfaction and positive affect, and less negative affect). Moreover, personality and well-being states dynamically influenced one another over time within individuals, and these associations were not generally moderated by dispositional traits or well-being. CONCLUSIONS: Behavior and well-being are interconnected within the context of the Big Five model of personality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".