Gratitude at the End of Life: A Promising Lead for Palliative Care
Bibliographic record
Abstract
BACKGROUND: Numerous studies, conducted largely with non-clinical populations, have shown a significant link between gratitude and psychological dimensions relevant for palliative care (e.g., psychological distress). However, the relevance of gratitude in the context of palliative care needs to be confirmed. OBJECTIVES: We strived to evaluate the association between gratitude and quality of life (QoL), psychological distress, post-traumatic growth, and health status in palliative patients, and to develop an explanatory model for QoL. An ancillary purpose was to identify which life domains patients considered sources of gratitude. DESIGN: We performed an exploratory and cross-sectional study with palliative patients of the Lausanne University Hospital. MEASUREMENTS: We used the Gratitude Questionnaire, the McGill Quality of Life questionnaire revised, the Hospital Anxiety and Depression Scale, the Post-traumatic Growth Inventory, and the health status items of the Eastern Cooperative Oncology Group. Spearman correlations and multivariate analyses were performed. RESULTS: Sixty-four patients participated (34 women, mean age = 67). The results showed significant positive correlations between gratitude and QoL (r = 0.376), and the appreciation of life dimension of the post-traumatic growth (r = 0.426). Significant negative correlations were found between gratitude and psychological distress (r = -0.324), and health status (r = -0.266). The best model for QoL explained 47.6% of the variance (F = 26.906) and included psychological distress and gratitude. The relational dimension was the most frequently cited source of gratitude (61%). CONCLUSION: Gratitude may act positively on QoL and may protect against psychological distress in the palliative situation. The next step will be the adaptation and implementation of a gratitude-based intervention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".