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Record W3120691319 · doi:10.1186/s12955-020-01643-2

Developing a short-form version of the HIV Disability Questionnaire (SF-HDQ) for use in clinical practice: a Rasch analysis

2021· article· en· W3120691319 on OpenAlexafffundabout
Kelly K. O’Brien, Mendwas Dzingina, Richard Harding, Wei Gao, Eve Namisango, Lisa Avery, Aileen M. Davis

Bibliographic record

VenueHealth and Quality of Life Outcomes · 2021
Typearticle
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsUniversity Health NetworkToronto Rehabilitation InstituteUniversity of Toronto
FundersUniversity of TorontoCanada Research ChairsBritish AcademyNational Institutes of HealthNational Institute on AgingKing's College London
KeywordsRasch modelDifferential item functioningCronbach's alphaItem response theoryQuality of life (healthcare)PromPatient-reported outcomeClinical psychologyHuman immunodeficiency virus (HIV)MedicinePsychologyPsychometricsFamily medicineDevelopmental psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Disability is an increasingly important health-related outcome to consider as more individuals are now aging with Human Immunodeficiency Virus (HIV) and multimorbidity. The HIV Disability Questionnaire (HDQ) is a patient-reported outcome measure (PROM), developed to measure the presence, severity and episodic nature of disability among adults living with HIV. The 69-item HDQ includes six domains: physical, cognitive, mental-emotional symptoms and impairments, uncertainty and worrying about the future, difficulties with day-to-day activities, and challenges to social inclusion. Our aim was to develop a short-form version of the HIV Disability Questionnaire (SF-HDQ) to facilitate use in clinical and community-based practice among adults living with HIV. METHODS: We used Rasch analysis to inform item reduction using an existing dataset of adults living with HIV in Canada (n = 941) and Ireland (n = 96) who completed the HDQ (n = 1037). We evaluated overall model fit with Cronbach's alpha and Person Separation Indices (PSIs) (≥ 0.70 acceptable). Individual items were evaluated for item threshold ordering, fit residuals, differential item functioning (DIF) and unidimensionality. For item threshold ordering, we examined item characteristic curves and threshold maps merging response options of items with disordered thresholds to obtain order. Items with fit residuals > 2.5 or less than - 2.5 and statistically significant after Bonferroni-adjustment were considered for removal. For DIF, we considered removing items with response patterns that varied according to country, age group (≥ 50 years versus < 50 years), and gender. Subscales were considered unidimensional if ≤ 5% of t-tests comparing possible patterns in residuals were significant. RESULTS: We removed 34 items, resulting in a 35-item SF-HDQ with domain structure: physical (10 items); cognitive (3 items); mental-emotional (5 items); uncertainty (5 items); difficulties with day-to-day activities (5 items) and challenges to social inclusion (7 items). Overall models' fit: Cronbach's alphas ranged from 0.78 (cognitive) to 0.85 (physical and mental-emotional) and PSIs from 0.69 (day-to-day activities) to 0.79 (physical and mental-emotional). Three items were rescored to achieve ordered thresholds. All domains demonstrated unidimensionality. Three items with DIF were retained because of their clinical importance. CONCLUSION: The 35-item SF-HDQ offers a brief, comprehensive disability PROM for use in clinical and community-based practice with adults living with HIV.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.034
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.203
GPT teacher head0.513
Teacher spread0.311 · 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 source (direct Gemma or distilled Codex), 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".

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Citations32
Published2021
Admission routes3
Has abstractyes

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