A Model of the Protective Value of Mindfulness in the Experience of Chronic Pain: Mediating Role of Pain Catastrophizing
Bibliographic record
Abstract
Abstract Objectives Dispositional mindfulness appears to exert beneficial effects in the experience of chronic pain and related symptoms. However, there is currently no widely accepted theoretical model that explains the mechanisms underlying this relationship. Furthermore, existing studies often lack sufficient theoretical justification, adequate sample sizes to ensure statistical power, and precise analytical methods to identify the specific mechanisms involved. Method The present study analyzed the adequacy of the recent model proposed by Wilson et al. (2023), according to which dispositional mindfulness (assessed via Five Facet Mindfulness Questionnaire) predicts reduced pain perception (assessed via Abbreviated McGill Pain Questionnaire) through decreased pain catastrophizing (Pain Catastrophizing Scale). The study aimed to replicate the original findings in a large sample of the general population ( n = 1075) and examine the model in a chronic pain population ( n = 467), in which it has not been previously analyzed. Our methodological approach employs structural equation modeling, allowing us to clarify the specific facets of mindfulness that most significantly contribute to this relationship. Results Dispositional mindfulness, particularly its facet of non-judgment of inner experience, significantly predicted lower pain scores through decreased pain catastrophizing, specifically in its helplessness dimension, both in the healthy subgroup and in those with chronic pain. These findings replicate those of Wilson et al. (2023) in the general sample and, notably, extend to the chronic pain sample as well. Conclusions Our results provide robust evidence supporting the model proposed by Wilson et al. (2023) and extend it by showing that within mindfulness a non-judgment attitude plays a crucial role in reducing helplessness when facing pain. These findings offer valuable insights for developing evidence-based interventions aimed at promoting health and managing chronic pain for which there is still no effective treatments options.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".