Operationalizing mHealth to improve patient care: a qualitative implementation science evaluation of the WelTel texting intervention in Canada and Kenya
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) applications have proliferated across the globe with much enthusiasm, although few have reached scale and shown public health impact. In this study, we explored how different contextual factors influenced the implementation, effectiveness and potential for scale-up of WelTel, an easy-to-use and evidence-based mHealth intervention. WelTel uses two-way SMS communication to improve patient adherence to medication and engagement in care, and has been developed and tested in Canada and Kenya. METHODS: We used a comparative qualitative case study design, which drew on 32 key informant interviews, conducted in 2016, with stakeholders involved in six WelTel projects. Our research was guided by the Consolidated Framework for Implementation Research (CFIR), a meta-theoretical framework, and our analysis relied on a modified approach to grounded theory, which allowed us to compare findings across these projects. RESULTS: We found that WelTel had positive influences on the "culture of care" at local clinics and hospitals in Canada and Kenya, many of which stretched beyond the immediate patient-client relationship to influence wider organizational systems. However, these were mediated by clinician norms and practices, the availability of local champion staff, the receptivity and capacity of local management, and the particular characteristics of the technology platform, including the ability for adaptation and co-design. We also found that scale-up was influenced by different forms of data and evidence, which played important roles in legitimization and partnership building. Even with robust research evidence, scale-up was viewed as a precarious and uncertain process, embedded within the wider politics and financing of Canadian and Kenyan health systems. Challenges included juggling different interests, determining appropriate financing pathways, maintaining network growth, and "packaging" the intervention for impact and relevance. CONCLUSIONS: Our comparative case study, of a unique transnational mobile health research network, revealed that moving from mHealth pilots to scale is a difficult, context-specific process that couples social and technological innovation. Fostering new organizational partnerships and ways of learning are paramount, as mHealth platforms straddle the world of research, industry and public health. Partnerships need to avoid the perils of the technological fix, and engage the structural barriers that mediate people's health and access to services.
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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 | 0.042 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".