Capacity to Describe Inner Experiences Predicts Lower Pain-Related Mind-Wandering during a Smartphone-Based Mindfulness Task in People with Chronic Pain
Bibliographic record
Abstract
Introduction/Aim: Pain-related mind-wandering (PRMW) may facilitate pain and disability in people with chronic pain. This study explored mindfulness skills that predict lower PRMW during mindfulness meditation (MM). Methods: 133 participants (AgeM = 20.5 years, SD = 3.74; Male = 45) were classified into groups: 1-Chronic pain (CP; n = 35) based on self-reported CP diagnoses, 2-Depressive-anxious (DA; n = 67) based on severe symptoms on the Center for Epidemiological Studies-Depression subscale (≥21) or Beck Anxiety Inventory (≥36), or 3-Controls (n = 31) if neither CP nor DA criteria applied. Participants completed the Five Facet Mindfulness Questionnaire (FFMQ) assessing mindfulness skills: observing experiences, describing internal experiences (Describe), acting with awareness, non-judgement and non-reactivity. Participants practiced breath-focused MM on a smartphone (~12-minutes) and pressed “breath” or “other” buttons at the sound of tones if awareness was on breathing or another experience, respectively. The Mind-Wandering Inventory was completed post-MM using 3 items: awareness of bodily pain, thoughts about pain, and other unpleasant sensations. Pearson correlations were conducted between FFMQ and PRMW. Results: Amongst all correlations, only Describe significantly predicted PRMW for CP (r = −0.46, p < 0.01), DA (r = 0.25, p < 0.05), and controls (r = 0.37, p < 0.05). CP participants had significantly higher % breath responses than the other groups. Discussion/Conclusions: The mindfulness skill of describing inner experiences may be a vital component of mindfulness-based pain treatments.
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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 | 0.000 | 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 | 0.000 |
| 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".