Russian Adaptation of the Pain Catastrophizing Scale
Bibliographic record
Abstract
The article presents the results of the adaptation of the Pain Catastrophizing Scale, which consists of three subscales (Rumination, Magnification and Helplessness) in a sample of 219 people (80,4% females). The average age of the participants was 22,05 years (median= 19,00 years, standard deviation=6,74; age range from 18 to 54 years). The Russian-language version of the Pain Catastrophizing Scale shows acceptable reliability for the scales (Cronbach’s alpha = 0,82; 0,67 and 0,83, respectively), and high reliability in general (Cronbach’s alpha = 0.89). Estimates of pain catastrophizing are positively correlated with the estimates of pain strength and intensity (McGill Pain Questionnaire, adapted by V. Kuzmenko in 1986), as well as with the level of self-control (Brief Self-Control Scale by J.P. Tangney, R.F. Baumeister and A.L. Boone, adapted by T. Gordeeva et al. in 2016): catastrophizing people, as a rule, have a lower level of self-control. Fit indices of confirmatory factor analysis (RMSEA=0,08; χ2/df=2.5; GFI=0.90; SRMR=0.7) characterize one- and three-factor models of pain catastrophizing as reasonable. Women showed a higher level of catastrophic pain in general, and differences were also found on the scale of Rumination, while there were no statistically significant differences on the scales of Magnification and Helplessness. Pain Catastrophizing is also positively correlated with anxiety and depression (HADS adapted by M. Drobizhev in 1993).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".