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Record W4323838569 · doi:10.1093/rap/rkad028

Psychometric testing of the British English Workplace Activity Limitations Scale in four rheumatic and musculoskeletal conditions

2022· article· en· W4323838569 on OpenAlexaff
Alison Hammond, Alan Tennant, Angela Ching, Jennifer Parker, Yeliz Prior, Monique A. M. Gignac, Suzanne Verstappen, Rachel O’Brien

Bibliographic record

VenueRheumatology Advances in Practice · 2022
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsInstitute for Work & HealthPublic Health OntarioUniversity of Toronto
FundersNorfolk and Norwich University Hospitals NHS Foundation TrustNational Institute for Health and Care ResearchSolent NHS TrustNorth Bristol NHS TrustSheffield Teaching Hospitals NHS Foundation TrustSwansea Bay University Health BoardNHS Greater Glasgow and Clyde
KeywordsRasch modelMedicineIntraclass correlationDebriefingPhysical therapyTest (biology)Scale (ratio)Reliability (semiconductor)Construct validityPsychometricsConcurrent validityDifferential item functioningInternal consistencyClinical psychologyPsychologyItem response theoryMedical educationDevelopmental psychologyCartography

Abstract

fetched live from OpenAlex

Abstract Objectives The aims were to validate a British English version of the Workplace Activity Limitations Scale (WALS) linguistically, then test this psychometrically in RA, axial spondyloarthritis (axSpA), OA and FM. Methods The WALS was forward translated, reviewed by an expert panel, and cognitive debriefing interviews were conducted. Participants completed a postal questionnaire booklet. Construct (structural) validity was examined by fit to the Rasch measurement model. Concurrent validity included testing between the WALS and the Work Limitations Questionnaire-25 (WLQ-25). Two weeks later, participants were mailed a second questionnaire booklet for test–retest reliability. Results Minor wording changes were made to the WALS, then 831 employed participants completed questionnaires: 267 men and 564 women; 53.5 (s.d. 8.9) years of age; with condition duration 7.7 (s.d. 8.0) years. The WALS satisfied Rasch model requirements, and a WALS Rasch transformation table was created. Concurrent validity was strong with the WLQ-25 (RA rs = 0.78; axSpA rs = 0.83; OA rs = 0.63; FM rs = 0.64). Internal consistency was consistent with group use (α = 0.80–0.87). Test–retest reliability was excellent, with intraclass correlation coefficient (2,1) at ≥0.90. Conclusion A reliable, valid British English version of the WALS is now available for use in the UK.

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)0.001
metaresearch head score (Gemma)0.025
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.310
Teacher spread0.294 · 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.

Study designObservational
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

Citations8
Published2022
Admission routes1
Has abstractyes

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