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Record W3180573324 · doi:10.1017/jgc.2021.11

Mindfulness-based intervention and aggression and rule-breaking behaviours in elementary school boys: a proof of concept trial

2021· article· en· W3180573324 on OpenAlexaff
Maedeh Heidary, Touraj Hashemi, Wolfgang Linden

Bibliographic record

VenueJournal of Psychologists and Counsellors in Schools · 2021
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMindfulnessAggressionIntervention (counseling)Proof of conceptPsychologyPsychotherapistBurden of proofSocial psychologyDevelopmental psychologyComputer sciencePsychiatryPolitical scienceLaw

Abstract

fetched live from OpenAlex

This study investigated the effect of a mindfulness-based intervention on pathological symptoms in boys with externalised disorders. A total of 24 elementary school students with externalising disorders, diagnosed by completing the Child Behavior Checklist (CBCL), were randomly assigned to a mindfulness-based intervention for two months. One session per week was offered and each session lasted one hour ( n = 12) or as a wait-list control group ( n = 12). Data were analysed via a multivariate analysis of covariance (MANCOVA) test. Students from the intervention group showed greater reductions in rule-breaking behaviours than those in the wait-list control group. Also, there was clear superiority of the intervention relative to the wait-list control group for reducing both aggression and rule-breaking behaviours (effect size differences were d = −2.52 and d = −1.88 respectively). These positive results on outcome measures provide initial evidence for a mindfulness-based intervention as a treatment option for boys with externalising disorders.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.031
GPT teacher head0.362
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; 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 designNon-randomized trial
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

Citations2
Published2021
Admission routes1
Has abstractyes

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