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Record W2167814723 · doi:10.1093/jpepsy/jsu007

The Effects of Mindful Attention and State Mindfulness on Acute Experimental Pain Among Adolescents

2014· article· en· W2167814723 on OpenAlexafffund
Mark Petter, Patrick J. McGrath, Christine T. Chambers

Bibliographic record

VenueJournal of Pediatric Psychology · 2014
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsCapital District Health Authority
FundersCanadian Institutes of Health Research
KeywordsMindfulnessPain catastrophizingMeditationPsychologyCoping (psychology)Situational ethicsClinical psychologyMindfulness meditationChronic painPain tolerancePsychotherapistPhysical therapyThreshold of painMedicinePsychiatrySocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: Attention-based coping strategies for pain are widely used in pediatric populations. The purpose of this study was to test a novel mindful attention manipulation on adolescent's experimental pain responses. Furthermore, the relationship between state mindfulness and experimental pain was examined. METHODS: A total of 198 adolescents were randomly assigned to a mindful attention manipulation or control group prior to an experimental pain task. Participants completed measures of state mindfulness immediately prior to the pain task, and situational catastrophizing and pain intensity following the task. RESULTS: Overall the manipulation had no effect on pain. Secondary analysis showed that meditation experience moderated the effect of the manipulation. State mindfulness predicted pain outcomes, with reductions in situational catastrophizing mediating this relationship. CONCLUSIONS: The mindful attention manipulation was effective among adolescents with a regular meditation practice. State mindfulness was related to ameliorated pain responses, and these effects were mediated by reduced catastrophizing.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.314
Teacher spread0.304 · 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 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

Citations37
Published2014
Admission routes2
Has abstractyes

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