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Unpacking the Black Box of Mindfulness: The Psychological Mechanisms of Mindfulness

2023· article· en· W4385222635 on OpenAlexaff
Mariana Toniolo–Barrios, Lieke Laura Ten Brummelhuis

Bibliographic record

VenueAcademy of Management Proceedings · 2023
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsUnpackingMindfulnessBlack boxPsychologyPsychotherapistMeditationClinical psychologyComputer scienceArtificial intelligenceGeography

Abstract

fetched live from OpenAlex

Whereas some people consider mindfulness a tool that allows individuals to step fully into their lives and approach their daily challenges more successfully, some consider mindfulness a tool that simply helps individuals not to be overly reactive to and overwhelmed by their daily experiences, and others even criticize it as being a practice that promotes passivity and numbness towards experiences. These contradictory views are also reflected in scholarly research on mindfulness, as there are theoretical claims asserting that mindfulness allows individuals to better respond to life situations whereas the definition of mindfulness characterizes it as being a neutral, non-evaluative state of mind. This raises the question of whether mindfulness makes individuals more active and agentic or more passive and neutral instead. To unravel the seemingly paradoxical effects of mindfulness, we systematically review empirical research on mindfulness to identify the psychological mechanisms of mindfulness. Then, based on the findings, we develop an organizing framework that identifies the major categories of mechanisms, and we put forward an integrative theoretical model to explain the psychological processes generated by mindfulness. Specifically, we posit that the mechanisms of mindfulness occur in a sequence of three overarching processes that unfold over time: dereification producing non-evaluative experiences in the very short run; reorientation producing positive modulated experiences in the short run; and internalization producing durable changes in one’s self-determined behavior in the long run. Finally, we explain how the model can inform future research.

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)0.004
metaresearch head score (Gemma)0.005
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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0020.026
Scholarly communication0.0070.012
Open science0.0010.004
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0030.000

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 head0.055
GPT teacher head0.350
Teacher spread0.295 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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