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Record W2120724926 · doi:10.1053/eujp.2000.0186

The development and validation of a Greek version of the short‐form McGill Pain Questionnaire

2000· article· en· W2120724926 on OpenAlexaboutno aff
George Georgoudis, P. J. Watson, Jacqueline Oldham

Bibliographic record

VenueEuropean Journal of Pain · 2000
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
Fundersnot available
KeywordsCronbach's alphaMcGill Pain QuestionnairePhysical therapyInternal consistencyChronic painReliability (semiconductor)PsychologyMusculoskeletal painMedicineGreek languageConsistency (knowledge bases)PsychometricsClinical psychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.969
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.236
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations55
Published2000
Admission routes1
Has abstractyes

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