The Hidden Power of “Thank You”: Exploring Aspects, Expressions, and the Influence of Gratitude in Religious Families
Bibliographic record
Abstract
Gratitude has been extensively studied over the past two decades. Among several predictors, aspects of religiosity and spirituality have been consistent predictors of gratitude. To explore the religious motivations and processes that foster the practice of gratitude, we undertook a systematic thematic analysis using interview data from a national qualitative project of 198 highly religious families. Participants (n = 476) included mothers, fathers, and children from various socioeconomic backgrounds and from diverse religious, racial, and ethnic backgrounds in the United States of America. Semi-structured interviews were conducted in the participants’ homes. Data for this study were analyzed using a team-based approach to qualitative analysis. The findings were organized thematically, including: (a) aspects of gratitude, (b) expressions of gratitude, and (c) the influence of gratitude. Two aspects of gratitude were identified: functional—what people were grateful for—and directional—to whom they were grateful. Expressions of gratitude involved participation in regular, gratitude-focused prayers and mutual day-to-day appreciation. The relational context and implications and context of gratitude in religious families were further examined and reported with sub-themes: (a) gratitude prompted positive re-evaluation of relationships and (b) gratitude reinforced religious faith. Implications, strengths, limitations, and future directions are discussed.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".