Development of a Blended Learning Approach to Delivering HIV-Assisted Contact Tracing in Malawi: Applied Theory and Formative Research
Bibliographic record
Abstract
BACKGROUND: Despite progress toward the Joint United Nations Programme on HIV/AIDS "95-95-95" targets (95% of HIV-positive persons tested, 95% of tested persons on treatment, and 95% of treated persons virally suppressed), a gap remains in achieving the first 95% target. Assisted contact tracing (ACT), in which health workers support HIV-positive index clients to recruit their contacts (sexual partners and children) for HIV testing, efficiently identifies HIV-positive persons in need of treatment. Although many countries, including Malawi, began implementing ACT, testing outcomes in routine settings have been worse than those in trial settings. OBJECTIVE: The aim of this paper is to use formative research and frameworks to develop and digitize an implementation package to bridge the gap between ACT research and practice. METHODS: Semistructured qualitative research was conducted in 2019 in Malawi with key informants. Barriers and facilitators to intervention delivery were identified using the Consolidated Framework for Implementation Research. Approaches to digitization were examined using human-centered design principles. RESULTS: Limited clinic coordination and health worker capacity to address the complexities of ACT were identified as barriers. Ongoing individual training consisting of learning, observing, practicing, and receiving feedback, as well as group problem-solving were identified as facilitators. Important features of digitization included (1) culturally relevant visual content, (2) capability of offline use, and (3) simple designs and basic editing to keep costs low. CONCLUSIONS: Formative research and frameworks played a key role in designing and digitizing an implementation package for ACT delivery in a low-income setting such as Malawi.
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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 | 0.036 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".