Gratitude and Well‐Being: Who Benefits the Most from a Gratitude Intervention?
Bibliographic record
Abstract
Background: Theory and research have shown that gratitude interventions have positive outcomes on measures of well‐being. Gratitude listing, behavioral expressions, and grateful contemplation are methods of inducing gratitude. While research has examined gratitude listing and behavioral expressions, no study has tested the long‐term effects of a gratitude contemplation intervention on well‐being. Methods: The present experiment examined the efficacy of a 4‐week gratitude contemplation intervention program in improving well‐being relative to a memorable events control condition. Pre‐test measures of cardiac coherence, trait gratitude, and positive and negative affect were collected. Pre‐ and post‐test measures assessing satisfaction with life and self‐esteem were also collected. Daily positive and negative affect were completed twice a week throughout the intervention period. Results: Compared to those in the memorable events condition, participants in the gratitude condition reported higher satisfaction with life and self‐esteem. Trait gratitude was found to moderate the effects of the gratitude intervention on satisfaction with life. Conclusion: Grateful contemplation can be used to enhance long‐term 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".