A mixed methods evaluation of 99DOTS digital adherence technology uptake among adolescents treated for pulmonary tuberculosis in Uganda.
Bibliographic record
Abstract
Introduction: Adolescents and young adults are at risk of poor adherence to tuberculosis (TB) treatment and subsequently worse TB treatment outcomes. Digital adherence technologies, including the mobile phone-based 99DOTS platform, can support TB treatment, but there is limited data on their use among adolescents. Objective: To evaluate factors associated with the uptake of 99DOTS among adolescents with TB. Methods: We conducted an explanatory sequential mixed methods study that utilized quantitative data from adolescents collected during the scale-up of 99DOTS at 30 health facilities in Uganda, and qualitative in-depth and key informant interviews with a subset of adolescents with pulmonary TB offered 99DOTS and healthcare providers at participating facilities. Findings were further mapped onto the Capability, Opportunity, Motivation, and Behavior (COM-B) model. Results: Overall, 299/410 (73%) eligible adolescents were enrolled in 99DOTS. Older adolescents 15 to 19 years old were more likely to enroll in 99DOTS than younger adolescents 10 to 14 years [aPR= 0.56, 95% CI: (0.42-0.73)]. Conversely, adolescents treated at Health Center IV and General Hospitals were less likely to be enrolled compared to Health Center III (aPR= 0.8, 95% CI, 0.67-0.94, and aPR=0.71, 95% CI 0.58-0.85, respectively). Technological savviness among older adolescents, access to training, caregiver or treatment supporter involvement, and desire for wellness facilitated the uptake of 99DOTS. In contrast, variable mobile phone access, concerns about TB status disclosure, and health worker workload in hospitals were barriers to the uptake of 99DOTS. Conclusion: 99DOTS uptake was high among adolescents with TB. Increased access to mobile phones, health worker training on adolescent communication, and more involvement of caregivers could facilitate greater use of 99DOTS and similar technologies.
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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 | 0.051 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".