RELATION BETWEEN SUBJECTIVE AND PHYSICAL WELL-BEING AND MINDFULNESS
Bibliographic record
Abstract
An individual's sense of well-being involves the complex interaction of psychological and health-related quality of life.Satisfaction with Life is reported subjectively and encompasses cognitive (assessment of life circumstances) and emotional (assessment of negative emotions) factors (Tay, Kuykendall, & Diener, 2015).Physical health is a more objective measure of overall physical and emotional functioning, social engagement, emotional well-being, energy levels, fatigue, pain, and general health perceptions (Hays & Morales, 2001).There is increasing evidence that mindfulness is associated with psychological and physical outcomes (Ludwig & Kabat-Zinn, 2008).Our purpose was to examine how different aspects of mindfulness (observing, describing, acting with awareness, non-judging of inner experience, and non-reactivity to inner experience) were related to physical and psychological well-being.In total, 513 non-clinical undergraduate participants completed questionnaires to measure life satisfaction, physical and psychological wellness and mindfulness.The current results highlight how personality and mindfulness affect both physical and psychological wellness.Specifically, emotional stability, extraversion, conscientiousness, and agreeableness were associated with better health outcomes and increased mindfulness.Some aspects of mindfulness (awareness and non-judging) were associated with both physical and psychological health.Given these results, we would suggest that individuals interested in improving their physical and psychological health might attend to an increased and non-judgemental focus on acting in the present moment.
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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 | 0.006 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".