Patients and Medical Staff Attitudes Toward the Future Inclusion of eHealth in Tuberculosis Management: Perspectives From Six Countries Evaluated using a Qualitative Framework
Bibliographic record
Abstract
BACKGROUND: Digitally delivering healthcare services is very attractive for tuberculosis (TB) management as this disease has a complex diagnosis and lengthy management and involves multiple medical and nonmedical specialists. Especially in low- and middle-income countries, eHealth could potentially offer cost-effective solutions to bridge financial, social, time, and distance challenges. OBJECTIVE: The goal of the research is to understand what would make eHealth globally applicable and gain insight into different TB situations, opportunities, and challenges. METHODS: We performed focus group interviews with TB experts and patients from 6 different countries on 4 different continents. The focus group interviews followed the theory of planned behavior framework to offer structured recommendations for a versatile eHealth solution. The focus group interviews were preceded by a general demographic and technology use questionnaire. Questionnaire results were analyzed using basic statistics in Excel (Microsoft Corporation). Focus group interview data were analyzed using ATLAS.ti 8 (ATLAS.ti Scientific Software Development GmbH) by assigning codes to quotations and grouping codes into the 5 domains within the framework. RESULTS: A total of 29 patients and 32 medical staff members were included in our study. All medical staff had used the internet, whereas 31% (9/61) of patients had never been online. The codes with the most quotations were information in relation to eHealth (144 quotations) and communication (67 quotations). The consensus among all participants from all countries is that there are important communication and information gaps that could be bridged by an eHealth app. Participants from different countries also highlighted different challenges, such as a majority of asylum-seeker patients or lack of infrastructure that could be addressed with an eHealth app. CONCLUSIONS: Within the 6 countries interviewed, there is high enthusiasm toward eHealth in TB. A potential app could first target information and communication gaps in TB, with additional modules aimed at setting-specific challenges.
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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 | 0.031 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".