Interventions to Improve HIV Viral Load Suppression among the Adolescents: Evidence of Improvement Science through a Quality Improvement Approach in Eastern Uganda
Bibliographic record
Abstract
Introduction: Achieving viral load suppression among the adolescents living with HIV continues to hold back attainment of sustainable development goals. TASO Mbale realized a viral load suppression rate of 63.1% among the adolescents living with HIV in care in quarter 4 of 2016. We therefore, instituted a quality imrpovement project to improve Viral load suppression from 63.1% in quarter 4 2016 to 90% by the end of quarter 4 2017. Method: Baseline data from the Uganda viral load dashboard were analyzed, and fishbone diagram was utilized to provide root causes of low viral load suppression among the adolescents living with HIV at TASO Mbale. The identified barriers were Knowlegde gap, among the adolescents, on positive living, Missing clinic appointments, Sub-optimal adherence, Poorly planned adolescent HIV clinic, Inadequate follow-up and Low use of data for informed decisions. A plan-do-study-act (PDSA) model was applied to implement tested changes. Strategies that worked included introduction of appointment register to track appointment behaviour of the adolescents, generating lists of clients on appointment who were due for Viral Load bleeding, telephone calls for follow up, increasing the frequency of reviewing adolescents from once a month to twice a week, committing a dedicated team responsible for adolescent care. Results: The viral load suppression improved from 63.1% in quarter 4 of 2016 to 63.8% in the first quarter of 2017, to 87.5% in quarter 2 of 2017, 97.6% in the third quarter and 91.4% in quarter 4 of 2017. Conclusion: The use of quality improvement in addressing gaps in HIV service delivery is highly effective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".