Factors associated with low antiretroviral therapy enrollment of children in the Simiyu region: A cross-sectional Creswell mixed-methods sequential explanatory design
Bibliographic record
Abstract
Despite substantial antiretroviral therapy (ART) coverage in other groups with the human immunodeficiency virus (HIV) in Tanzania, there is a progressive decline in ART enrollment among HIV-infected children. This study aimed to determine the factors affecting the enrollment of children with HIV in ART and to identify an effective, sustainable intervention to address children's ART care enrollment. To achieve this, we conducted a cross-sectional study using a mixed-method sequential explanatory design, including children with HIV aged 2 to 14 years in the Simiyu region. Stata™ and NVIVO™ software were used to perform quantitative and qualitative data analyses, respectively. In the quantitative analyses, we considered 427 children, with a mean age of 8.54 ± 3.54 years and a median age of 3 years (interquartile range: 1-6 years). The mean length of ART initiation delay was 3.71 ± 3.21 years. Additionally, independent child enrollment predictors included distance to the facility (adjusted odds ratio [AOR]: 3.31; 95% confidence interval [CI]:1.14-9.58), caregivers' income (AOR: 0.17; 95% CI: 0.07-0.43), and fear of stigma (AOR: 3.43; 95% CI: 1.14-10.35). In qualitative analyses, 36 respondents reported that stigma, distance, and lack of HIV-positive status disclosure to their fathers were causes for low enrollment in ART. Overall, this study demonstrated that a caregiver's income, distance to obtain HIV care services, HIV-positive status non-disclosure to the father, and fear of stigma played a significant role in children's enrollment in HIV care. As such, HIV/acquired immunodeficiency syndrome programs would benefit from having intensive interventions to address distance, such as scaling up care and treatment centers, as well as techniques to reduce stigma in the population.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".