Factors Influencing Adherence to Antiretroviral Therapy (ART) among Adolescents Living with Human Immunodeficiency virus (HIV) in Rwanda
Bibliographic record
Abstract
Background: HIV continues to be an important public health concern among adolescents. To reduce the high rate of mortality and improve the quality of life among people with HIV, WHO guidelines emphasize the early initiation of ART drugs in HIV-infected persons regardless of their CD4 count and clinical status. However, adherence to ART remains low in adolescents between 10 to 19 years from low and middle-income countries (LMICs). Objective: To determine the factors influencing adherence to ART among adolescents with HIV in Rwanda. Method: A cross-sectional design using proportional stratified random sampling to select 166 adolescents was conducted. Data were analyzed using descriptive and inferential statistics with a p-value <0.05 and a CI of 95%. Results: The overall adherence to ARTs was 38%. Assistance of clinical staff in taking medication (p<0.001) and the help of parents in taking medication (p<0.001) positively influenced adherence to ART. Insufficient health care providers, forgetfulness (p=0.009), and dosage too complex (p=0.044) negatively influenced adherence to ART. Conclusion: Factors such as some one reminding adolescents to take medication, non-stigmatization, and absence of side effects were positively associated with ART adherence. On the other hand, forgetfulness, complex dosage, being isolated and inadequate education about medications negatively affect adherence to ARTs. There is a need to set strategies to increase adherence to ARTs, including expert clients and trustable guardians in care provision. All adolescents should receive adequate counselling and health education before the initiation of ARTs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".