Psychometric properties of the Short-Form McGill Pain Questionnaire (SF-MPQ) in adult Mexican cancer patients with chronic pain
Bibliographic record
Abstract
Abstract Background Pain is a frequent symptom in cancer patients (CP), and its multidimensional assessment is essential for a comprehensive approach and to establish clinical prognoses. The Short-Form McGill Pain Questionnaire (SF-MPQ) is an internationally recognized tool for the multidimensional assessment of pain, both in clinical and research settings. However, no studies have been reported in Latin America that determine its psychometric properties in CP and chronic pain. Objectives To determine the psychometric properties of the SF-MPQ in adult Mexican cancer patients with chronic pain. Methods An instrumental design was used with a non-probabilistic convenience sample of 222 cancer patients treated at the pain clinic of a tertiary care hospital. Analyses were conducted to evaluate factorial structure (exploratory and confirmatory factor analysis [CFA]), reliability (internal consistency), measurement invariance, and criterion validity (concurrent and divergent). Results CFA verified a 9-item structure divided into 2 factors: (1) Affective-Nociceptive and (2) Neuropathic. A global Cronbach’s alpha coefficient of .82 and a global McDonald’s Omega index of .82 were identified. Configural, metric, and scalar invariance (ΔCFI ≤ .01; ΔRMSEA ≤ .015) were confirmed regarding the sex variable. Finally, the SF-MPQ showed a positive correlation with the Numerical Rating Scale (rho = .436, p< .01) and a negative correlation with the EORTC-QLQ C30 (rho = −.396, p< .01). Significance of results The Mexican version of the SF-MPQ presented adequate psychometric properties and fit indices, making it a valid and reliable instrument for use in clinical and research settings in Mexico. Its use is recommended for the comprehensive assessment of pain in oncology in Mexico, as it allows for the understanding of pain characteristics beyond intensity, guiding the establishment of clinical prognoses.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".