The Relative Contribution of Mindfulness and Gratitude in Predicting Happiness among University Students
Bibliographic record
Abstract
The present study aims at identifying the separate and interactive contribution of gratitude and mindfulness in predicting happiness; examining the relationship between these variables; identifying differences between students with high happiness and students with low happiness in gratitude and mindfulness; and identifying the levels of gratitude, mindfulness, and happiness among the students of Princess Nourah Bint Abdulrahman University. The research sample consisted of 447 female students aged 18-25 years. The research instruments included the Toronto Mindfulness Scale, the Oxford Happiness Questionnaire, as well as the Gratitude, Resentment, and Appreciation Test-Short form. The study found out that gratitude and mindfulness had a significant contribution in predicting happiness among university students (31% and 41.5%, respectively). The interaction between the total scores of mindfulness and gratitude contributed 51.5% of the variance in happiness among university students. The interaction between mindfulness, sense of abundance, and simple appreciation contributed 54.4% of the variance in happiness among university students. The study found a positive correlation between mindfulness, gratitude (sense of abundance, simple appreciation, appreciation of others), and happiness. Additionally, it was found that students at Nourah Bint Abdulrahman University had moderate levels of mindfulness and moderate to high levels of gratitude and happiness. The sense of abundance domain was moderate, the simple appreciation domain was high, and the appreciation of others domain was moderate. Mindfulness, gratitude, sense of abundance, simple appreciation, and appreciation of others increased among the students with high happiness. Received: 8 March 2021 / Accepted: 22 June 2021 / Published: 8 July 2021
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".