Analysis of factors influencing health-relatedquality of life in HIV/AIDS patients
Bibliographic record
Abstract
Introduction: Patients with human immunodeficiency viruses (HIV)/acquired immune deficiency syndrome (AIDS) face a wide range of physical and mental issues.Hhealth-related quality of life (HRQoL) has to be given more attention, since HIV/AIDS epidemic has not been fully addressed.Study participants were those living with HIV/AIDS, who wanted to learn more about variables, which influence their HRQoL.Material and methods: Using a cross-sectional technique, the study was performed in a descriptive analytic style.Overall sample size was 101 respondents, who were selected using a basic random sampling procedure, based on inclusion criteria.Characteristics of participants in this research, including income, duration of sickness, ease in accessing care and infrastructure, and presence of family and peer support, served as independent factors.Health-related quality of life was the dependent variable in this research.A questionnaire was employed as a testing tool.Logistic regression statistical test was applied, with a significance threshold of p = 0.05. Results:According to the findings of the present study, there was a correlation between income (p = 0.044), infrastructure (p = 0.003), social support (p = 0.009), and health-related quality of life (p = 0.009).An individual's HRQoL was most strongly influenced by the presence of adequate infrastructure (Exp(B) = 38.549),social support (Exp(B) = 31.575),and a stable source of income (Exp(B) = 26.764).Conclusions: HRQoL of people living with HIV/AIDS can be affected by variables, including income, infrastructure, and social support.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".