The Clarification of Depression and Social Support's Contribution to the Prediction of Antiretroviral Medication Adherence and the Rate of CD4 in People with HIV
Bibliographic record
Abstract
With the development of the antiretroviral therapy, the number of the people with HIV is increasing; therefore, identifying the factors affecting HIV is of great importance. This study aimed to investigate the relationship between the antiretroviral medication adherence and the rate of CD4 with depression and social support in the people with HIV. The research method was a descriptive study kind of correlation. The statistical population included all patients with HIV in Shiraz, of whom, 220 people who had referred to the Behavioral Diseases Consultation Center were selected using the available sampling method. Philips et al.'s Social Support Questionnaire, Beck's Depression Questionnaire II, and ACTG Medication Adherence Questionnaire were used as the research tools. Results were analyzed using the stepwise regression and stepwise hierarchical multiple regression. Regression analysis showed that social support and depression variables could predict totally 47% (P<0.001) of changes of medication adherence variable, and depression could predict only 2% (P<0.01) of rate variance of CD4.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".