Gratitude uniquely predicts lower depression in chronic illness populations: A longitudinal study of inflammatory bowel disease and arthritis.
Bibliographic record
Abstract
OBJECTIVE: Although gratitude has been identified as a key clinically relevant trait for improving well-being, it is understudied within medical populations. The current study addressed this gap and extended previous and limited cross-sectional research by examining the longitudinal associations of gratitude to depression in 2 chronic illness samples, arthritis and inflammatory bowel disease (IBD). METHODS: Two chronic illness samples, arthritis (N = 423) and IBD (N = 427), completed online surveys at Time 1 (T1). One hundred sixty-three people with arthritis and 144 people with IBD completed the 6-month follow-up survey (T2). Depression, gratitude, illness cognitions, perceived stress, social support, and disease-related variables were assessed at T1 and T2. RESULTS: At T2, 57.2% of the arthritis sample and 53.4% of the IBD sample met the cut off scores for significant depression. T1 gratitude was negatively associated with depressive symptoms at T1 and T2 in both samples (rs from -.43 to -.50). Regression analyses revealed that T1 gratitude remained a significant and unique predictor of lower T2 depression after controlling for T1 depression, relevant demographic variables, illness cognitions, changes in illness-relevant variables, and another positive psychological construct, thriving, in both samples. CONCLUSION: As the first investigation of the longitudinal associations of gratitude to psychological well-being in the context of chronic illness, the current study provides important evidence for the relevance of gratitude for health-related clinical populations. Further intervention-based research is warranted to more fully understand the potential benefits of gratitude for adjustment to chronic illness. (PsycINFO Database Record
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".