Disentangling trait versus state characteristics of the Pain Catastrophizing Scale and the PHQ‐8 Depression Scale
Bibliographic record
Abstract
BACKGROUND: Research on the role of trait versus state characteristics of a variety of measures among persons experiencing pain has been a focus for the past few decades. Studying the trait versus state nature of the Pain Catastrophizing Scale (PCS) and the Patient Health Questionnaire (PHQ-8) depression scale would be highly informative given both are commonly measured in pain populations and neither scale has been studied for trait/state contributions. METHODS: The PHQ-8 and PCS were obtained on persons undergoing knee arthroplasty at baseline, 2-, 6- and 12-month post-surgery (N = 402). The multi-trait generalization of the latent trait-state model was used to partition trait and state variability in PCS and PHQ-8 item responses simultaneously. A set of variables were used to predict trait catastrophizing and trait depression. RESULTS: For total scores, the latent traits and latent states explain 63.2% (trait = 43.2%; state = 20.0%) and 50.2% (trait = 29.4%; state = 20.8%) of the variability in PCS and PHQ-8, respectively. Patients with a high number of bodily pain sites, high levels of anxiety, young patients and African-American patients had high levels of trait catastrophizing and trait depression. The PCS and the PHQ-8 consist of both enduring trait and dynamic state characteristics, with trait characteristics dominating for both measures. CONCLUSION: Clinicians and researchers using these scales should not assume the obtained measurements solely reflect either trait- or state-based characteristics. SIGNIFICANCE: Clinicians and researchers using the PCS or PHQ-8 scales are measuring both state and trait characteristics and not just trait- or state-based characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".