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Record W4405911869 · doi:10.24042/tadris.v9i2.23655

Mindfulness-Based Cognitive Behavioral Interventions to Enhance Academic Buoyancy: A Meta-Analytic Study

2024· article· en· W4405911869 on OpenAlexaboutno aff
Anisa Mawarni, Agus Taufiq, Ilfiandra Ilfiandra, Faizal Faizal, Fariza Makmun

Bibliographic record

VenueTadris Jurnal Keguruan dan Ilmu Tarbiyah · 2024
Typearticle
Languageen
FieldPsychology
TopicGrit, Self-Efficacy, and Motivation
Canadian institutionsnot available
Fundersnot available
KeywordsMindfulnessPsychological interventionPsychologyPsychotherapistCognitionMeta-analysisCognitive psychologyMedicineNeurosciencePsychiatry

Abstract

fetched live from OpenAlex

This study explores the effectiveness of mindfulness-based cognitive behavioral interventions in developing academic buoyancy among students, defined as their capacity to overcome daily academic challenges such as stress, time pressure, and minor failures. Using a meta-analytic approach, the research synthesizes findings from 15 independent studies involving 4,509 participants across diverse educational contexts, including the United States, Canada, Iran, and the United Kingdom. The random effects model revealed a significant positive correlation between mindfulness interventions and academic buoyancy, with a high effect size. The findings underscore the potential of mindfulness integrated with cognitive behavioral therapy to enhance students' resilience by reducing stress and improving executive functions such as emotional regulation, attention, and decision-making. Despite its demonstrated effectiveness, heterogeneity in study designs, intervention durations, and measurement tools suggests the need for further research to refine and standardize these interventions. This study provides evidence-based insights for educators and counselors aiming to integrate mindfulness practices into academic settings, contributing to the broader discourse on mental health and educational resilience.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.127
GPT teacher head0.457
Teacher spread0.330 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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