The Mindful Reappraisal of Pain Scale: Initial Validity Evidence for Use as a State Measure Following Isometric Exercise
Bibliographic record
Abstract
The Mindful Reappraisal of Pain Scale (MRPS) measures the capacity to reinterpret pain mindfully, supporting resilience and persistence during discomfort. The MRPS may be especially useful in acute, exercise-related pain contexts, where individuals experience short-term but physically demanding discomfort. This study is the first to evaluate the validity of a state-based version of the MRPS in an acute exercise context. Physically active participants (N = 127) completed a plank and wall sit and reported state mindful reappraisal of pain, trait and state mindfulness, and exercise experience perceptions. Confirmatory factor analyses supported a unidimensional structure with good internal consistency (ω = 0.88/0.90). MRPS scores correlated with mindfulness, pain tolerance, and affect and uniquely predicted wall sit pain tolerance after controlling for mindfulness. Scores were unrelated to pain intensity and perceived exertion, supporting the theoretical distinction of cognitive–affective reinterpretation rather than sensory attenuation. These findings support the MRPS as a brief, reliable tool for assessing mindful reappraisal in acute exercise contexts while also aligning with emerging evidence from clinical validation studies. However, further research is needed to confirm psychometric robustness across diverse exercise modes and participant populations.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".