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Record W2913948382 · doi:10.21926/obm.icm.1901003

Cultivating Well-Being through the Three Pillars of Mind Training: Understanding How Training the Mind Improves Physiological and Psychological Well-Being

2019· article· en· W2913948382 on OpenAlexaff
Andrew Villamil, Talya Vogel, Elli Weisbaum, Daniël J. Siegel

Bibliographic record

VenueOBM Integrative and Complementary Medicine · 2019
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsCanada Research ChairsUniversity of TorontoUniversity of New Brunswick
Fundersnot available
KeywordsPsychologyTraining (meteorology)MindfulnessEnergy (signal processing)NeuroplasticityNeural correlates of consciousnessNeuroimagingCognitive psychologyCognitive scienceNeuroscienceCognitionPsychotherapist

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.000

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.

Opus teacher head0.185
GPT teacher head0.383
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations67
Published2019
Admission routes1
Has abstractyes

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