Measuring Health-Related Quality of Life (HRQOL) in HIV-Positive Individuals-Content Analyses of Measures Based on the International Classification of Functioning, Disability, and Health (ICF) and Generic and Disability Core Set
Bibliographic record
Abstract
© 2015 by Begell House, Inc. Purpose: The purpose of the study is to conduct a content analysis of the items of the Medical Outcome Study-HIV (MOS-HIV), the Multidimentional Quality of Life-HIV (MQOL-HIV), and the HIV Disability Questionnaire (HDQ) by linking these to the ICF and its generic and disability core set. Methods: Four raters individually linked 145 items from the measures based on standardized linking rules. Interrater agreement was determined. For items where there was no agreement among raters, the opinion of a fifth rater, an expert in applying the ICF, was obtained. Results: The items were linked to 74 ICF categories across the three measures. Final interrater agreement was 83.3%. The content of all three measures was highly linkable with ICF. The HDQ had the best representation among the three measures. The HDQ had 27 items linked with the generic and disability core set; of these 16 items were linked with disability codes. Discussion: The HDQ provides a more precise description of the disablement experienced by people living with HIV by addressing the body function, activities, participation, and environmental factors specific to people living with HIV. The HDQ is thus recommended for those who are interested in measuring HIV-specific disabilities more precisely.
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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 | 0.078 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".