Establishing the Reliability, Validity, and Prognostic Utility of the Momentary Pain Catastrophizing Scale for use in Ecological Momentary Assessment Research
Bibliographic record
Abstract
Despite the marked increase in Ecological Momentary Assessment (EMA) research, few reliable and valid measures of momentary experiences have been established. The goal of this preregistered study was to establish the reliability, validity, and prognostic utility of the momentary Pain Catastrophizing Scale (mPCS), a 3-item measure developed to assess situational pain catastrophizing. Participants in two studies of postsurgical pain outcomes completed the mPCS three to five times per day prior to surgery (N = 494, T = 20,271 total assessments). The mPCS showed good psychometric properties, including multilevel reliability and factor invariance across time. Participant-level average mPCS was strongly positively correlated with dispositional pain catastrophizing as assessed by the Pain Catastrophizing Scale (r = .55 and .69 in Study 1 and Study 2, respectively). To establish prognostic utility, we then examined whether the mPCS improved prediction of postsurgical pain outcomes above and beyond one-time assessment of dispositional pain catastrophizing. Indeed, greater variability in momentary pain catastrophizing prior to surgery was uniquely associated with increased pain immediately after surgery (b = 0.58, p = 0.005), after controlling for preoperative pain levels and dispositional pain catastrophizing. Greater average mPCS score prior to surgery was also uniquely associated with lesser day-to-day improvement in postsurgical pain (b = 0.01, p = 0.003), whereas dispositional pain catastrophizing was not (b = -0.007, p = 0.099). These results show that the mPCS is a reliable and valid tool for EMA research and highlight its potential utility over and above retrospective measures of pain catastrophizing.
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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 | 0.027 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".