Shesha, a WhatsApp Chatbot for Linking Household Contacts to Tuberculosis Treatment or Preventive Therapy in South Africa: Design and Development
Bibliographic record
Abstract
Background: Literature on the development of mobile health (mHealth) tools for public health interventions is scarce. This scarcity creates a knowledge gap, and new tools may repeat the mistakes of past implementations. Objective: In this paper, we describe the development of Shesha, a WhatsApp-based chatbot designed to facilitate linkage to care for household contacts of people being treated for tuberculosis (TB). Shesha facilitates linkage by providing TB test results, TB preventive treatment (TPT) information, nudges, reminders, and personalized support. We developed Shesha to address the human resource capacity challenges posed by South Africa's new universal TB testing and TPT policies. Methods: We applied a design thinking framework with 7 phases: empathize, discover, define, prototype, build and launch, improve, and evaluate. The process started with gathering insights from TB contact tracing studies and consulting with global and local experts to address the challenges of universal TB testing and TPT. Based on these findings, we defined the core functionalities of Shesha and incorporated them in the Health Belief Model to encourage health-seeking behavior. In collaboration with the developers, we developed the WhatsApp-based chatbot. We selected WhatsApp for its wide accessibility and user-friendliness. Results: We successfully developed and launched the Shesha in September 2023, with implementation expected to continue until March 2025. Early user acceptance revealed that users generally valued the information provided on the tool regarding TB and TPT; however, they required ongoing engagement to link to care. Ongoing evaluations, guided by the Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) framework, will assess the tool's impact on reducing community health worker workloads and improving linkage to care. Conclusions: Documenting the development of mHealth technologies is crucial for guiding future projects and improving health interventions. In our study, frameworks like design thinking and the Health Belief Model aligned Shesha with user needs and programmatic goals. Comprehensive documentation may help assess the chatbot's performance and guide future improvements, supporting scalability and efficiency in mHealth interventions across public health settings.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".