Digital Adherence Technologies and Differentiated Care for Tuberculosis Treatment and Their Acceptability Among Persons With Tuberculosis, Health Care Workers, and Key Informants in the Philippines: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: Digital adherence technologies (DATs) are being studied to determine their potential to support tuberculosis (TB) treatment and address the shortcomings of directly observed therapy. Previous research has shown inconclusive results on whether DATs can enhance medication adherence among persons with TB. OBJECTIVE: This study aims to understand the acceptability of DATs, namely, medication labels and smart pillboxes, among persons with TB, health care workers (HCWs), and key informants (KIs) in the Philippines. The objective is to gain valuable insights that can inform the design and implementation of DATs in the Southeast Asian region, which meet the needs and preferences of end users. METHODS: Persons with TB, HCWs, and KIs were recruited from intervention facilities to participate in in-depth interviews conducted between March 2022 and January 2023. These interviews were transcribed and translated into English. A thematic analysis was carried out using NVivo software (Lumivero) to identify and analyze themes. Themes were then structured within a modified social-ecological model. RESULTS: A total of 25 persons with drug-sensitive TB and 20 HCWs or KIs were interviewed. Both groups emphasized that users' technology literacy level, financial conditions, and motivation to be cured determined how they interacted with the DAT. They also acknowledged that DATs helped foster their relationship with HCWs and enabled efficient treatment support. Concerning technology, persons with TB found DATs easy to use and able to reduce clinic visits. HCWs mentioned that DATs added to their workload but also allowed them to support users who missed doses. However, both groups experienced technical challenges with DATs. Regarding program implementation, users appreciated the clear explanations and demonstrations provided by HCWs. Yet, some users reported inconsistencies between DAT settings and the information provided. HCWs stressed the importance of comprehensive training and sufficient resources for effective program implementation in the future. At the community level, both groups noted that DATs and program design protected users' privacy and reduced the risk of stigma. Finally, users and HCWs shared various contextual factors that influenced their experience with DAT, including infrastructure challenges and the impact of the COVID-19 pandemic. CONCLUSIONS: In the Philippines, persons with TB and HCWs showed a high level of acceptance and satisfaction with the impact of DAT and program design. They expressed a desire for the continuation of DATs. The challenges encountered underscore the need for ongoing technological development to minimize malfunctions, enhance the capacity of health facilities, and improve infrastructure. DATs have demonstrated their ability to strengthen user-HCW relationships and protect users from stigmatization. Additional efforts are required to scale up the DAT program in the Philippines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".