Psychological Burden and Medication Adherence of Human Immunodeficiency Virus Positive Patients
Bibliographic record
Abstract
BACKGROUND: Highly active antiretroviral therapy (HAART), which is a combination therapy that uses antiretroviral drugs, represents the only available therapy for combating the human immunodeficiency virus (HIV) infection. For Korean patients with HIV, it is difficult to maintain an optimal medication. The HIV- positive Korean patients that suffer from psychological burdens have low levels of medication adherence, which can lead to an increased mortality rate and a deteriorated quality of life. AIM: The purpose of this study was to investigate the level of medication adherence of Korean patients with HIV, and to identify the pathway through which the psychological burden impact medication adherence. METHODS: With a sample of 265 HIV-positive patients, the direct and indirect effects of the psychological burden on treatment adherence were estimated with structural equation modeling. RESULTS: The variable that had a significant direct effect on medication adherence was self-management (β = .31, p = .002). Psychological burden was found to have a significant impact on medication adherence mediated by self-management (β = -.15, p = .002). CONCLUSION: These results revealed that continuous monitoring of patients’ psychological burdens, as well as the development and application of intervention programs focused on psychological support and self-management, are necessary to improve medication adherence.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".