Integrating Humor and Positive Psychology Approaches to Psychological Well-Being
Bibliographic record
Abstract
In this study we investigated how individual differences and personality constructs taken from the positive psychology and humor domains of psychology may play an important role in psychological well-being. Participants completed measures assessing trait gratitude, savoring, and humor styles; along with several positive and negative indicators of psychological well-being (e.g., life satisfaction, positive affect, depression, and anxiety). We first examined the degree of empirical and conceptual overlap among the personality constructs from these two domains. Here, we found that higher levels of gratitude and savoring were associated with higher levels of self-enhancing and affiliative humor, whereas higher levels of aggressive and self-defeating humor were primarily associated with lower levels of gratitude. Subsequent regression analyses indicated that the positive psychology construct of gratitude was predictive of several different indices of positive and negative well-being, whereas savoring was most predictive of greater positive affect. In addition, these regression analyses also revealed that the humor styles of self-enhancing and self-defeating humor provided a significant increase in the prediction of several positive and negative indices of well-being, above and beyond the effects attributable to the positive psychology constructs alone. These findings were then discussed in terms of developing a broader and more integrated theoretical approach to the understanding of psychological well-being.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".