Characterizing the disability experience among adults living with HIV: a structural equation model using the HIV disability questionnaire (HDQ) within the HIV, health and rehabilitation survey
Bibliographic record
Abstract
Abstract Background People aging with HIV can experience a variety of health challenges associated with HIV and multimorbidity, referred to as ‘disability’. Our aim was to characterize the disability experience and examine relationships between dimensions of disability among adults living with HIV. Methods We performed a structural equation modeling analysis with data from the Canadian web-based HIV, Health and Rehabilitation Survey. We measured disability using the HIV Disability Questionnaire (HDQ), a patient-reported outcome (69 items) that measures presence, severity and episodic features of disability across six domains: 1) physical symptoms, 2) cognitive symptoms, 3) mental-emotional health symptoms, 4) difficulties carrying out day-to-day activities, 5) uncertainty and worrying about the future, and 6) challenges to social inclusion. We used HDQ severity domain scores to represent disability dimensions and developed a structural model to assess relationships between disability dimensions using path analysis. We determined overall model fit with a Root Mean Square Error of Approximation (RMSEA) of 0.5 a large (strong) effect. We used Mplus software for the analysis. Results Of the 941 respondents, most (79%) were men, taking combination antiretroviral medications (90%) and living with two or more simultaneous health conditions (72%). Highest HDQ presence and severity scores were in the uncertainty domain. The measurement model had good overall fit (RMSEA= 0.04). Results from the structural model identified physical symptoms as a strong direct predictor of having difficulties carrying out day-to-day activities (standardized path coefficient: 0.54; p
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".