Effectiveness of a community-based intervention (Konga model) to address the factors contributing to viral load suppression among children living with HIV in Tanzania: a cluster-randomized clinical trial protocol
Bibliographic record
Abstract
Abstract This study aims to test the effectiveness of a community-based intervention (Konga model) to improve viral-load suppression in children living with human immunodeficiency virus (HIV) and enrolled in care and treatment centers in Tanzania mainland. The study will be a cluster-randomized clinical trial study designed with both intervention and control arms. The study will involve 268 children with a viral load of >1000 copies/ml who are aged between 2 and 14 years. The children will be randomly allocated into the intervention and control arms. The intervention will include three distinct activities: adherence and retention counseling, psychosocial support, and comorbidity screening (i.e. tuberculosis). The outcome of the study will be assessment of the success of the intervention to increase medication adherence with the immediate result of reducing the viral load below 1000 copies/ml. Descriptive statistics will be used to calculate the mean, median, standard deviation, and interquartile range of continuous data. We will use frequencies and percentages to summarize categorical data. As for the primary outcome (proportion of HIV-infected children with viral suppression), we will compare the proportion of successful participants in the intervention and control arms. Proportions and tests for different proportions will be used as a measure of improvement. All statistical tests will be two-sided and P < 0.05 will be considered statistically significant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".