Factors Influencing Non-Adherence to Antiretroviral Therapy Among HIV/AIDS Patients in Western Sumatra: Implications for Practice in the Post-COVID-19 Era
Bibliographic record
Abstract
This study seeks to elucidate the factors contributing to non-adherence to antiretroviral therapy (ART) among HIV/AIDS patients in Western Sumatra, with a view to informing practice in the post-COVID-19 era.Utilizing a mixed-methods approach that combines qualitative analyses and survey techniques, in-depth interviews were conducted with 26 patients at M. Djamil Hospital, serving a diverse patient population across Western Sumatra, Indonesia.Non-adherence was frequently attributed to factors such as medication-related boredom, forgetfulness, busyness, geographic inaccessibility to healthcare facilities, stigma, economic constraints, insurance challenges, and concerns related to COVID-19.Additionally, adverse effects of the medication, including dizziness, nausea, vomiting, sleepiness, irregular heartbeat, rash, and diarrhea were reported.Furthermore, the critical role of non-governmental organizations (NGOs) in patient support through education, medication delivery, and home visits was identified.The findings underscore a complex interplay of behavioral, socio-economic, and systemic factors underpinning non-adherence.It is posited that healthcare providers, by recognizing these determinants, can develop targeted interventions to enhance adherence, such as personalized communication strategies, including phone calls and home visits, in alignment with the UNAIDS program's long-term objectives.This study contributes to the existing literature by highlighting the multifaceted reasons behind ART nonadherence in a post-pandemic context, suggesting a need for comprehensive strategies that address both individual and structural barriers to optimize treatment outcomes for HIV/AIDS patients.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".