Cultivating Well-Being through the Three Pillars of Mind Training: Understanding How Training the Mind Improves Physiological and Psychological Well-Being
Bibliographic record
Abstract
Research on the physiological and emotional health benefits of meditative practices has grown exponentially over the last two decades, influencing both scientific literature and popular media. Research has highlighted three distinct components or pillars at the core of meditative practices and mind training. They are, focused attention, open awareness, and kind intention. Neuroimaging studies and recent research highlight that the repeated practice of directing attention and awareness can enhance neural connections, and turn momentary mindful states into more enduring mindful traits. Most meditative practices typically only engage one or two of these elements, and there has been no identified meditative practice that integrates all three pillars that we are aware of, except for a concept referred to as the “Wheel of Awareness”. The Wheel is a practical framework for understanding and practicing mindful awareness, and is unusual because it engages all three pillars in one practice, shaping how energy and information flow from one component to the next one. Through conscious practice individuals can improve the ability to observe and reflect on the mind, increasing the ability to monitor and modify neural networks, which in turn modulate physiological responses within the body. Further research is proposed to further understand the neurobiological underpinnings behind repeated practice, including longitudinal studies monitoring neuroplasticity and activity in establishing new neural connections and synaptic changes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".