Confirmatory factor analysis of the Korean version of the short-form McGill pain questionnaire with chronic pain patients: a comparison of alternative models
Bibliographic record
Abstract
BACKGROUND: The Short Form of the McGill Pain Questionnaire (SF-MPQ) is the most widely used assessment of the quality and intensity of pain. In previous validation studies, the factor structure of the SF-MPQ varied widely from various two-factor structures to a five-factor structure, although research on the SF-MPQ quite consistently supports its two-factor structure (i.e., sensory and affective) across different countries and languages. In Korea, the results of exploratory factor analysis of a Korea version of SF-MPQ (KSF-MPQ) showed 2-factor structure consisting of 'sensory' and 'affective' excluding two items such as splitting and heavy. As an attempt to further validate the KSF-MPQ, the purpose of this study was to confirm whether the KSF-MPQ model is an appropriate model for chronic pain patients in Korea by comparing several alternative models of the SF-MPQ. FINDINGS: A total of 150 chronic pain patients seeking treatment in Seoul, Korea, participated and completed the KSF-MPQ. Confirmatory factor analysis was conducted to evaluate the adequacy of the KSF-MPQ model and several alternative models. The results indicated that the adjusted KSF-MPQ model showed the best fit to the data among the models in chronic pain patients in Korea. CONCLUSIONS: The results showed the KSF-MPQ is cross-culturally equivalent to the original questionnaire. Thus, the KSF-MPQ is valid measurement for assessing the quality and intensity of pain to chronic pain patients and may be helpful in clinical and research settings in Korea.
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 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 | 0.057 | 0.114 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".