THE ASSOCIATION BETWEEN GRATITUDE AND MORTALITY AMONG OLDER US WOMEN
Bibliographic record
Abstract
Abstract Gratitude, as a positive emotion, is a potentially modifiable psychological factor that may enhance healthy aging. However, the association between gratitude and mortality has not been studied. Using data from the Nurses’ Health Study (N=52,169 U.S. female nurses, mean age=79 years, 2016 questionnaire wave to December 2019), Cox proportional hazards regression models estimated the hazard ratio of deaths by self-reported levels of gratitude at baseline. Gratitude was assessed with the 6-item Gratitude Questionnaire, a validated measure of one’s general tendency to experience grateful affect. Deaths were identified from the National Death Index, state statistics records, reports by next of kin, and the postal system. Causes of death were ascertained by physicians through reviewing death certificates and medical records. Over 158,374 person-years of follow-up, 5,343 incident deaths were identified. Greater gratitude was associated with a substantially lower hazard of mortality. For instance, the highest vs. lowest tertile of gratitude was associated with a 10% lower hazard of all-cause deaths (95% confidence interval of hazard ratio: 0.82, 0.97), adjusting for baseline sociodemographic characteristics, social participation, religious involvement, physical health, lifestyle factors, and mental health. When considering cause-specific deaths, gratitude was inversely associated with each specific cause of death. This inverse association was strongest with deaths due to cardiovascular disease. This study provides the first empirical evidence suggesting that experiencing grateful affect is associated with a lower risk of mortality in later life. Interventions designed to increase the experience of grateful affect may provide a novel avenue for increasing longevity among older adults.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".