Modeling the mindfulness‐to‐meaning theory's mindful reappraisal hypothesis: Replication with longitudinal data from a randomized controlled study
Bibliographic record
Abstract
The Mindfulness to Meaning Theory provides a detailed process model of the mechanisms by which mindfulness may promote well-being. Central to the Mindfulness to Meaning Theory is the mindful reappraisal hypothesis (MRH), which suggests mindfulness training promotes well-being by facilitating positive reappraisal. Emerging evidence from interconnected domains of research supports the MRH. However, it remains unclear whether mindful reappraisal continues to develop after a mindfulness training course and whether this continued development encourages well-being over time. As such, this randomized controlled study compared participants receiving a mindfulness-based stress reduction (MBSR) course with participants receiving no mindfulness training on positive reappraisal use and well-being over the course of 6 years. Latent growth curve modeling revealed that mindfulness training increased well-being by significantly increasing the trajectory of positive reappraisal over time. The MRH was then unpacked by examining whether MBSR also stimulated decentering and broadened awareness, core components of the MRH. Multivariate path analysis revealed that mindfulness training increased decentering, which in turn broadened awareness, which was then associated with positive reappraisal, ultimately promoting well-being. Taken together, these findings suggest that MBSR cultivates a downstream cascade of adaptive psychological processes that continue to promote quality of life 6-years after mindfulness training.
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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 | 0.138 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".