A validation study of the Cantonese Chinese version of short form <scp>McGill</scp> pain questionnaire 2 in Cantonese‐speaking patients with chronic pain in Hong Kong
Bibliographic record
Abstract
Abstract Objective The study tests the reliability and validity of the Cantonese Chinese version of Short Form McGill Pain Questionnaire 2 (SF‐MPQ‐2‐CC). Methods The original Short Form McGill Pain Questionnaire (SF‐MPQ‐2) was translated into Cantonese Chinese version. Cantonese‐speaking chronic pain patients from three pain centers in Hong Kong were recruited and asked to complete SF‐MPQ‐2‐CC, validated Chinese versions of Identification Pain questionnaire (ID Pain), Pain Catastrophizing Scale (PCS), and Short Form Health Survey (SF‐36) for evaluation of convergent and divergent validity, 2 weeks apart for evaluation of internal consistency. Results A total of 333 and 197 participants completed the first and second set of questionnaires, respectively. SF‐MPQ‐2‐CC was shown to have excellent internal consistency, with an overall Cronbach's alpha value of 0.933. The overall correlation coefficient was 0.875 that shows good test–retest reliability. Construct validity was evaluated using confirmatory factor analysis, where a seconder‐order factor model demonstrated a good fit with our data (χ2 = 826.51, p < 0.001, CFI = 0.92, TLI = 0.908, RMSEA = 0.097; SRMR = 0.063; error terms adjusted). SF‐MPQ‐2‐CC also showed good convergent validity with Chinese versions of ID Pain (neuropathic pain) and PCS (continuous pain), and divergent validity was shown by a negative correlation with Chinese version of SF‐36. Conclusions Our study demonstrated that SF‐MPQ‐2‐CC is a valid and reliable pain assessment tool for Cantonese‐speaking patients in Hong Kong with a wide range of chronic pain conditions. It also helps to identify the presence of neuropathic pain and negative pain cognition among respondents.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".