Perceptions of drones, digital adherence monitoring technologies and educational videos for tuberculosis control in remote Madagascar: a mixed-method study protocol
Bibliographic record
Abstract
INTRODUCTION: Poor road and communication infrastructure pose major challenges to tuberculosis (TB) control in many regions of the world. TB surveillance and patient support often fall to community health workers (CHWs) who may lack the time or knowledge needed for this work. To meet the End TB Strategy goal of reducing TB incidence by 90% by 2035, the WHO calls for intensified research and innovation including the rapid uptake of new tools, interventions and strategies. Technologies that 'leapfrog' infrastructure challenges and support CHWs in TB control responsibilities have the potential to dramatically change TB outcomes in remote regions. Such technologies may strengthen TB control activities within challenged national tuberculosis treatment and control programmes (NTPs), and be adapted to address other public health challenges. The deployment of innovative technologies needs to be differentially adapted to context-specific factors. The Drone Observed Therapy System (DrOTS) project was launched in Madagascar in 2017 and integrates a bundle of innovative technologies including drones, digital adherence monitoring technology and mobile device-based educational videos to support TB control. METHODS AND ANALYSIS: This mixed-methods study gathers and analyses cultural perceptions of the DrOTS project among key stakeholders: patients, community members, CHWs, village chiefs and NTP-DrOTS mobile health teams. Data from questionnaires, semistructured interviews, focus group discussions (FGD) and ethnographic observation gathered from June 2018 to June 2019 are thematically analysed and compared to identify patterns and singularities in how DrOTS stakeholders perceive and interact with DrOTS technologies, its enrolment processes, objectives and team. ETHICS AND DISSEMINATION: Ethics approval was obtained from the National Bioethics Research Committee of Madagascar and Stony Brook University institutional review board. Study results will be submitted for peer-reviewed publication. In Madagascar, results will be presented in person to Ministry and other Malagasy decision-makers through the Institut Pasteur de Madagascar. PATIENT AND PUBLIC INVOLVEMENT: This study is designed to foreground the voices of patients and potential patients in the DrOTS programme. CHW participants in this study also supported the design of study information sessions and recruitment strategies. One member of the mobile health team provided detailed input on the wording and content of FGD and interview guides. Study findings will be presented via a report in French and Malagasy to CHW, mobile health team and other village-level participants who have email/internet access.
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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 | 0.032 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".