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Record W2180982102 · doi:10.1080/1047840x.2015.1064294

Mindfulness Broadens Awareness and Builds Eudaimonic Meaning: A Process Model of Mindful Positive Emotion Regulation

2015· article· en· W2180982102 on OpenAlexaff
Eric L. Garland, Norman A. S. Farb, Philippe R. Goldin, Barbara L. Fredrickson

Bibliographic record

VenuePsychological Inquiry · 2015
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsUniversity of Toronto
FundersNational Institute on Drug AbuseNational Institutes of Health
KeywordsMindfulnessPsychologyMeaning (existential)EudaimoniaProcess (computing)Social psychologyPsychotherapistPositive psychologyCognitive psychologyEpistemology

Abstract

fetched live from OpenAlex

Contemporary scholarship on mindfulness casts it as a form of purely nonevaluative engagement with experience. Yet, traditionally mindfulness was not intended to operate in a vacuum of dispassionate observation, but was seen as facilitative of eudaimonic mental states. In spite of this historical context, modern psychological research has neglected to ask the question of how the practice of mindfulness affects downstream emotion regulatory processes to impact the sense of meaning in life. To fill this lacuna, here we describe the mindfulness-to-meaning theory, from which we derive a novel process model of mindful positive emotion regulation informed by affective science, in which mindfulness is proposed to introduce flexibility in the generation of cognitive appraisals by enhancing interoceptive attention, thereby expanding the scope of cognition to facilitate reappraisal of adversity and savoring of positive experience. This process is proposed to culminate in a deepened capacity for meaning-making and greater engagement with life.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.008
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.160
GPT teacher head0.412
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations792
Published2015
Admission routes1
Has abstractyes

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