Contextual factors influencing implementation of tuberculosis digital adherence technologies: a scoping review guided by the RE-AIM framework
Bibliographic record
Abstract
INTRODUCTION: Digital adherence technologies (DATs) may enable person-centred tuberculosis (TB) treatment monitoring; however, implementation challenges may undermine their effectiveness. Using the reach, effectiveness, adoption, implementation and maintenance framework, we conducted a scoping review to identify contextual factors informing 'reach' (DAT engagement by people with TB) and 'adoption' (DAT uptake by healthcare providers or clinics). METHODS: We searched eight databases from 1 January 2000 to 25 April 2023 to identify all TB DAT studies. After extracting qualitative and quantitative findings, using thematic synthesis, we analysed common findings to create meta-themes informing DAT reach or adoption. Meta-themes were further organised using the Unified Theory of Acceptance and Use of Technology, which posits technology use is influenced by perceived usefulness, ease of use, social influences and facilitating conditions. RESULTS: 66 reports met inclusion criteria, with 61 reporting on DAT reach among people with TB and 27 reporting on DAT adoption by healthcare providers. Meta-themes promoting reach included perceptions that DATs improved medication adherence, facilitated communication with providers, made people feel more 'cared for' and enhanced convenience compared with alternative care models (perceived usefulness) and lowered stigma (social influences). Meta-themes limiting reach included literacy and language barriers and DAT technical complexity (ease of use); increased stigma (social influences) and suboptimal DAT function and complex cellular accessibility challenges (facilitating conditions). Meta-themes promoting adoption included perceptions that DATs improved care quality or efficiency (perceived usefulness). Meta-themes limiting adoption included negative DAT impacts on workload or employment and suboptimal accuracy of adherence data (perceived usefulness); and suboptimal DAT function, complex cellular accessibility challenges and insufficient provider training (facilitating conditions). Limitations of this review include the limited studies informing adoption meta-themes. CONCLUSION: This review identifies diverse contextual factors that can inform improvements in DAT design and implementation to achieve higher engagement by people with TB and healthcare providers, which could improve intervention effectiveness.
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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 | 0.064 | 0.169 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.034 | 0.029 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".