Pain Sensitivity and Analgesic Effects of Mindful States in Zen Meditators: A Cross-Sectional Study
Bibliographic record
Abstract
OBJECTIVE: To investigate pain perception and the potential analgesic effects of mindful states in experienced Zen meditators. METHODS: Highly trained Zen meditators (n = 13; >1000 hours of practice) and age/gender-matched control volunteers (n = 13) received individually adjusted thermal stimuli to elicit moderate pain on the calf. Conditions included: a) baseline-1: no task; b) concentration: attend exclusively to the calf; c) mindfulness: attend to the calf and observe, moment to moment, in a nonjudgmental manner; and d) baseline-2: no task. RESULTS: Meditators required significantly higher temperatures to elicit moderate pain (meditators: 49.9 degrees C; controls: 48.2 degrees C; p = .01). While attending "mindfully," meditators reported decreases in pain intensity whereas control subjects showed no change from baseline. The concentration condition resulted in increased pain intensity for controls but not for meditators. Changes in pain unpleasantness generally paralleled those found in pain intensity. In meditators, pain modulation correlated with slowing of the respiratory rate and with greater meditation experience. Covariance analyses indicated that mindfulness-related changes could be partially explained by changes in respiratory rates. Finally, the meditators reported higher tendencies to observe and be nonreactive of their own experience as measured on the Five Factor Mindfulness Questionnaire; these factors correlated with individual differences in respiration. CONCLUSIONS: These results indicated that Zen meditators have lower pain sensitivity and experience analgesic effects during mindful states. Results may reflect cognitive/self-regulatory skills related to the concept of mindfulness and/or altered respiratory patterns. Prospective studies investigating the effects of meditative training and respiration on pain regulation are warranted.
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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 | 0.001 |
| 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 | 0.000 |
| 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".