Dispositional Mindfulness and Acute Heat Pain: Comparing Stimulus-Evoked Pain With Summary Pain Assessment
Bibliographic record
Abstract
OBJECTIVE: Dispositional mindfulness is associated with reduced pain in clinical and experimental settings. However, researchers have neglected the type of pain assessment, as dispositional mindfulness may have unique benefits for reduced pain sensitivity when relying on summary pain assessments, in contrast to assessing the pain of each noxious stimulus. Here, we test the association between dispositional mindfulness and pain using both trial-by-trial pain assessments and overall summary ratings after acute pain tasks. METHODS: One hundred thirty-one healthy adult volunteers (mean age = 29.09 [8.00] years, 55.7% female) underwent two experimental thermal pain paradigms. We tested whether dispositional mindfulness measured with the Mindful Attention Awareness Scale was related to a) heat-evoked pain sensitivity, as measured by pain threshold, pain tolerance, average pain, trial-by-trial ratings, and heat-evoked skin conductance response, and b) summary judgments of sensory and affective pain assessed using the McGill Pain Questionnaire (MPQ). RESULTS: Mindful Attention Awareness Scale ratings were associated with decreased pain on the MPQ sensory (B = -0.18, SE = 0.05, 95% confidence interval = -0.29 to -0.07, t = -3.28, p = .001) and affective (B = -0.11, SE = 0.03, 95% confidence interval = -0.18 to -0.05, t = -3.32, p = .001) dimensions but not with experimental thermal pain assessments, including threshold, tolerance, heat-evoked pain, or skin conductance response (p values ≥ .29). CONCLUSIONS: In this study, dispositional mindfulness mitigated acute thermal pain only when pain was assessed using the MPQ. These findings may reflect differences in immediate versus retrospective judgments or the type of pain assessed by each measure. Future research should examine regulation processes that may explain these differential analgesic benefits, such as attention, rumination, or reappraisal.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".