The development and validation of a Greek version of the short‐form McGill Pain Questionnaire
Bibliographic record
Abstract
BACKGROUND: The short form of the McGill Pain Questionnaire (SFMPQ) is a widely used instrument for assessing the quality of pain where use of the full form is not possible. To date however, this instrument has not been translated into the Greek language. AIMS: It is the aim of this study to validate an adopted Greek version of the short form of the McGill Pain Questionnaire. METHODS: A systematic translation procedure was followed before development of the final version. Sixty spinal and osteoarthritis chronic musculoskeletal pain patients completed the questionnaire. A large percentage of the subjects (43%) was of elementary educational level. RESULTS: The analysis of the results indicated that an internally consistent (Cronbach's alpha = 0.71) and content valid (all 15 descriptors were used at least by the 33% of the subjects) instrument has been developed. It has been shown to be suitable, easy to understand and administer for this sample of chronic musculoskeletal patients. CONCLUSIONS: A Greek version of the SFMPQ (the GR-SFMPQ) has been constructed which has the properties of internal validity and consistency. It is easy to administer, easy to understand even for an elementary educational level and it is capable of describing multidimensionally the pain experience of chronic musculoskeletal pain patients.
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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 | 0.012 | 0.032 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".