The Many Roads to Mindfulness: A Review of Nonmindfulness-Based Interventions that Increase Mindfulness
Bibliographic record
Abstract
Objectives: Mindfulness-based interventions (MBIs) have become increasingly popular for treating various physical and mental disorders. An increase in mindfulness levels through the teaching of mindfulness meditation is the most well-studied mechanism of MBIs. Recent studies, however, suggest that an increase in mindfulness is also observed in physical or psychosocial interventions not explicitly labeled as MBIs, or what the authors call non-MBIs. The authors aimed to review what non-MBIs can increase mindfulness levels despite not explicitly teaching mindfulness meditation. Design: The authors conducted a literature search for studies that included a non-MBI study arm measuring pre- and postintervention mindfulness levels using one of the following eight validated self-reported mindfulness questionnaires: Five-Faceted Mindfulness Questionnaire, Mindful Awareness and Attention Scale, Freiburg Mindfulness Inventory, Toronto Mindfulness Scale, Philadelphia Mindfulness Scale, Kentucky Inventory of Mindfulness Skills, Child and Adolescent Mindfulness Measure, and Cognitive and Affective Mindfulness Scale-Revised. The authors identified 69 non-MBI study arms from 51 independent studies of the non-MBI itself or as active controls of an MBI under investigation. The authors documented or calculated, if not provided, effect sizes (ES) for changes in mindfulness levels following these interventions. Results: Among the 69 non-MBI arms, 36 showed no effect for change in mindfulness (ES <0.20), 3 were indeterminate (no ES available or unable to calculate), 13 had small effects (0.20 < ES <0.5), 13 had medium effects (0.50 < ES <0.80), 3 had large effects (0.80 < ES <1.3), and 1 had a very large effect (ES >1.3) for change in mindfulness. Conclusions: Analysis of the characteristics of non-MBIs with significant increases in mindfulness levels suggested some commonalities between MBIs and non-MBIs, shedding light on a spectrum of mindfulness-related interventions and the possibility that there are many roads to developing mindfulness.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".