Cognitive Fusion and Pain Experience in Young People
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".