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Record W4376132204 · doi:10.1037/emo0001252

An experimental test of the mindfulness-to-meaning theory: Casual pathways between decentering, reappraisal, and well-being.

2023· article· en· W4376132204 on OpenAlexafffund
Yiyi Wang, Eric L. Garland, Norman A. S. Farb

Bibliographic record

VenueEmotion · 2023
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
KeywordsMindfulnessPsychologyMindsetCognitive reappraisalPsychological interventionPsycINFOClinical psychologyModerated mediationWell-beingPsychotherapistDevelopmental psychologySocial psychologyCognitionMEDLINE

Abstract

fetched live from OpenAlex

= 131) employed a four-arm randomized trial design, featuring (a) control, (b) mindfulness, (c) stress mindset, and (d) blended mindfulness and stress mindset training conditions. The MMT pathway accounted for change in well-being across all models, mindfulness training consistently promoted positive reappraisal despite an absence of reappraisal instructions, and an exploratory cross-lagged analysis found decentering facilitative of subsequent reappraisal. However, the stress mindset intervention failed to improve well-being relative to control, limiting capacity for causal inference; post hoc analyses, therefore, focused on the more efficacious mindfulness training conditions. The MMT accounted for change in well-being across all levels of analysis, although well-being changes were also supported by direct effects of mindfulness training and decentering, with only partial mediation through the complete MMT pathway. These findings support MMT as a process model for well-being but suggest that decentering and reappraisal only partially account for the salutary effects of well-being interventions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.686
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.034
GPT teacher head0.338
Teacher spread0.304 · 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 designObservational
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

Citations23
Published2023
Admission routes2
Has abstractyes

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