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Record W4391897565 · doi:10.1111/hex.13978

Content validity testing of the INTERMED Self‐Assessment in a sample of adults with rheumatoid arthritis and rheumatology healthcare providers

2024· article· en· W4391897565 on OpenAlexafffund
Kiran Dhiman, Marc Hall, Trafford Crump, Alison M. Hoens, Diane Lacaille, James A. Rankin, Karen L. Then, Glen Hazlewood, Cheryl Barnabé, Steven J. Katz, Jason M. Sutherland, Erika Dempsey, Claire Barber

Bibliographic record

VenueHealth Expectations · 2024
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsResearch CanadaUniversity of British ColumbiaUniversity of AlbertaAlberta Bone and Joint Health InstituteUniversity of Calgary
FundersInstitute of Musculoskeletal Health and ArthritisCanadian Institutes of Health Research
KeywordsMedicineHealth careBiopsychosocial modelCLARITYContent validityRheumatologyFamily medicineLikert scaleInternal medicinePsychologyPsychometricsClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Care complexity can occur when patients experience health challenges simultaneously with social barriers including food and/or housing insecurity, lack of transportation or other factors that impact care and patient outcomes. People with rheumatoid arthritis (RA) may experience care complexity due to the chronicity of their condition and other biopsychosocial factors. There are few standardised instruments that measure care complexity and none that measure care complexity specifically in people with RA. OBJECTIVES: We assessed the content validity of the INTERMEDS Self-Assessment (IMSA) instrument that measures care complexity with a sample of adults with RA and rheumatology healthcare providers (HCPs). Cognitive debriefing interviews utilising a reparative framework were conducted. METHODS: Patient participants were recruited through two existing studies where participants agreed to be contacted about future studies. Study information was also shared through email blasts, posters and brochures at rheumatology clinic sites and trusted arthritis websites. Various rheumatology HCPs were recruited through email blasts, and divisional emails and announcements. Interviews were conducted with nine patients living with RA and five rheumatology HCPs. RESULTS: Three main reparative themes were identified: (1) Lack of item clarity and standardisation including problems with item phrasing, inconsistency of the items and/or answer sets and noninclusive language; (2) item barrelling, where items asked about more than one issue, but only allowed a single answer choice; and (3) timeframes presented in the item or answer choices were either too long or too short, and did not fit the lived experiences of patients. Items predicting future healthcare needs were difficult to answer due to the episodic and fluctuating nature of RA. CONCLUSIONS: Despite international use of the IMSA to measure care complexity, patients with RA and rheumatology HCPs in our setting perceived that it did not have content validity for use in RA and that revision for use in this population under a reparative framework was unfeasible. Future instrument development requires an iterative cognitive debriefing and repair process with the population of interest in the early stages to ensure content validity and comprehension. PATIENT OR PUBLIC CONTRIBUTION: Patient and public contributions included both patient partners on the study team and people with RA who participated in the study. Patient partners were involved in study design, analysis and interpretation of the findings and manuscript preparation. Data analysis was structured according to emergent themes of the data that were grounded in patient perspectives and experiences.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.065
GPT teacher head0.339
Teacher spread0.274 · 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 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

Citations3
Published2024
Admission routes2
Has abstractyes

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