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Record W4416390467 · doi:10.3390/bs15111586

The Mindful Reappraisal of Pain Scale: Initial Validity Evidence for Use as a State Measure Following Isometric Exercise

2025· article· en· W4416390467 on OpenAlexaff
Sara Thompson, R. D. Small, Anne E. Cox, Sarah Ullrich‐French

Bibliographic record

VenueBehavioral Sciences · 2025
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTraitConfirmatory factor analysisInternal consistencyMindfulnessPsychometricsConstruct validityAffect (linguistics)Isometric exercisePain catastrophizing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.275
GPT teacher head0.494
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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