Thriving at Work: A Meta-Analysis
Bibliographic record
Abstract
Thriving at work refers to a positive psychological state characterized by a joint sense of vitalityand learning. Based on Spreitzer and colleagues’ (2005) model, we present a comprehensive meta-analysis of antecedents and outcomes of thriving at work (K = 73 independent samples, N = 21,739 employees). Results showed that thriving at work is associated with individual characteristics, such as psychological capital (rc = .47), proactive personality (rc = .58), positive affect (rc = .52), and work engagement (rc = .64). Positive associations were also found between thriving at work and relational characteristics, including supportive coworker behavior (rc = .42), supportive leadership behavior (rc= .44), and perceived organizational support (rc = .63). Moreover, thriving at work is related to important employee outcomes, including health-related outcomes like burnout (rc = -.53), attitudinal outcomes like commitment (rc = .65), and performance-related outcomes like task performance (rc = .35). The results of relative weights analyses suggest that thriving exhibits small, albeit incremental predictive validity above and beyond positive affect and work engagement, for task performance, job satisfaction, subjective health, and burnout. Overall, the findings of this meta-analysis support Spreitzer and colleagues’ (2005) model and underscore the importance of thriving in the work context.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.031 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".