Bibliographic record
Abstract
Researchers and practitioners alike are increasingly interested in the potential benefits of perceiving work as meaningful. Nonetheless, the strengths of these effect sizes are unknown. The purpose of this paper is to provide a meta-analytical examination of the relationships between work meaningfulness and a variety of work- and life-related outcomes. We meta-analyzed these relationships across 146 independent samples, representing a total sample of N = 70,541. The results indicated that work meaningfulness was strongly associated with a variety of positive outcomes, including heightened motivation (ρ =.55), organizational commitment (ρ =.56), work engagement (ρ =.62), job satisfaction (ρ =.66), hope (ρ =.62), efficacy (ρ =.56), job performance (ρ =.31), positive affect (ρ =.55), work relationships (ρ =.35), citizenship behaviors (ρ =.45), life meaning (ρ =.45) and overall life satisfaction (ρ =.48). Increased perceptions of meaningful work were also strongly negatively related to turnover intentions (ρ =-.39), burnout (ρ =-.40), stress (ρ =-.29), and counterproductive behaviors (ρ =-.41). These results emphasize the strong correlations between the experienced meaningfulness of work and a diversity of positive work-related outcomes. Suggestions for future research in this area are discussed.
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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 | 0.039 | 0.088 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.040 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".