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Record W2333485773 · doi:10.1097/ajp.0000000000000227

Cognitive Fusion and Pain Experience in Young People

2015· article· en· W2333485773 on OpenAlexaff
Ester Solé, Catarina Tomé‐Pires, Rocío de la Vega, Mélanie Racine, Elena Castarlenas, Mark P. Jensen, Jordi Miró

Bibliographic record

VenueClinical Journal of Pain · 2015
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsSt Joseph's Health CareLawson Health Research InstituteWestern University
Fundersnot available
KeywordsPain catastrophizingCognitionMedicineConfidence intervalClinical psychologyPhysical therapyDistressPsychologyChronic painPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: Acceptance and Commitment Therapy (ACT) has been shown to be an effective treatment for chronic pain in young people. Cognitive fusion is a key concept of ACT that is hypothesized to contribute to distress and suffering. In this study, we sought to: (1) test hypothesized associations between cognitive fusion and pain intensity, disability, and catastrophizing; and (2) examine the function of cognitive fusion as a possible mediator between catastrophizing and disability. METHODS: A community sample of 281 young people (11 to 20 y) completed measures assessing cognitive fusion, pain intensity, disability, and pain catastrophizing. RESULTS: Cognitive fusion was positively related to pain intensity (r=0.24, P<0.01), disability (r=0.32, P<0.001), and pain catastrophizing (r=0.47, P<0.001). Moreover, cognitive fusion was found to mediate the association between pain catastrophizing and disability (β=0.01, 95% confidence interval=0.002-0.024, 5000 bootstrap resamples). DISCUSSION: The findings indicate that cognitive fusion is moderately to strongly associated with pain-related outcomes, which support the need for further research to (1) better understand the relationship between cognitive fusion and adjustment to chronic pain, and (2) determine whether the benefits of treatments such as ACT are mediated, at least in part, by reductions in cognitive fusion.

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)0.020
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.021
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)0.0000.000

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 head0.091
GPT teacher head0.422
Teacher spread0.332 · 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

Citations49
Published2015
Admission routes1
Has abstractyes

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