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Record W239383866 · doi:10.4471/ijep.2013.28

Reductions in Negative Automatic Thoughts in Students Attending Mindfulness Tutorials Predicts Increased Life Satisfaction

2013· article· en· W239383866 on OpenAlexaff
Paul Ritvo, Khushboo Vora, Jane Irvine, Myriam Mongrain, Saam Azargive, Muhammad Abid Azam, Meysam Pirbaglou, Crissa L. Guglietti, Noah Wayne, Daniel F Perez, Robert A. Cribbie

Bibliographic record

VenueInternational Journal of Educational Psychology · 2013
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsYork University
Fundersnot available
KeywordsMindfulnessMeditationLife satisfactionPsychologyMental healthMindfulness meditationClinical psychologyMedical educationStudent lifeApplied psychologyPsychotherapistMedicineComputer science

Abstract

fetched live from OpenAlex

University education confronts students with stressful developmental challenges that can lead to mental health problems. Innovative programs must address an increasing prevalence of these problems but are impeded by the high costs involved. In this study, thirty-nine undergraduate students attended weekly one hour mindfulness meditation tutorials during a single (14 week) semester. Tutorials involved 40 minutes of guided meditation, followed by open-ended discussions on mindfulness and related scientific research. Multiple regression analysis tested associations between self-reported changes in mindfulness, in negative automatic thoughts and in satisfaction with life.Reductions in automatic thoughts accounted for a significant proportion of variance in life satisfaction and decreases in automatic thoughts were associated with an increased life satisfaction. This finding suggests guided meditation tutorials merit consideration in promoting student mental health on university campuses.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0340.000

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 head0.417
Teacher spread0.383 · 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

Citations20
Published2013
Admission routes1
Has abstractyes

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