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Record W4396564425 · doi:10.2196/54117

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

2024· article· en· W4396564425 on OpenAlexvenueno aff
Chung Lam Leung, Jason Alacapa, Bianca Gonçalves Tasca, Andre Villanueva, Saniata Masulit, Marvin Louie Ignacio, Kathleen Nicole Uy, Christopher Pell, Kristian van Kalmthout, Rachel Powers, Katherine Fielding, Degu Jerene

Bibliographic record

VenueJMIR Human Factors · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintQualitative researchTuberculosisHealth careKey (lock)MedicineFamily medicineNursingPsychologySociologyEconomic growthComputer scienceWorld Wide WebComputer securityPathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.069
GPT teacher head0.438
Teacher spread0.370 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2024
Admission routes1
Has abstractyes

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