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Record W4413043898 · doi:10.1016/j.jcbs.2025.100927

Effects of an ACT-based intervention on university students’ self-compassion and psychological well-being

2025· article· en· W4413043898 on OpenAlexafffund
Mireille Joussemet, Jean‐Michel Robichaud, Geneviève A. Mageau, Simon Grégoire

Bibliographic record

VenueJournal of Contextual Behavioral Science · 2025
Typearticle
Languageen
FieldComputer Science
TopicEducation and Learning Interventions
Canadian institutionsUniversité de MontréalUniversité de MonctonUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCompassionPsychologySelf-compassionIntervention (counseling)Social psychologyPsychotherapistMindfulnessApplied psychologyPsychoanalysisClinical psychologyTheologyPsychiatryPhilosophy

Abstract

fetched live from OpenAlex

This study aimed to evaluate the effects of an ACT intervention, Korsa, on university students’ self-compassion and psychological well-being. We also explored whether self-compassion could mediate Korsa’s well-being benefits. In this randomized controlled trial, 137 university students were randomly assigned to Korsa or a waitlist control condition. Participants completed pre- and post-intervention questionnaires about their self-compassion and well-being (i.e., life satisfaction and presence of meaning). Results showed that compared to students on the waitlist, participants assigned to the Korsa intervention reported higher life satisfaction, meaning, and self-compassion at post-intervention. Exploratory analyses provided preliminary support for the hypothesis that enhancing self-compassion could be a promising mechanism through which Korsa may improve psychological well-being among university students. Bridging the self-compassion and ACT frameworks seems to be a fruitful avenue to advance knowledge about the various ACT benefits and its potential mechanisms of change.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

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

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 head0.024
GPT teacher head0.377
Teacher spread0.352 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes2
Has abstractyes

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