Identifying Barriers and Facilitators of 13 mHealth Projects in North America and Africa: Protocol for a 5-Year Implementation Science Study
Bibliographic record
Abstract
BACKGROUND: Although many mHealth interventions have shown efficacy in research, few have been effectively implemented and sustained in real-world health system settings. Despite this programmatic gap, there is limited conclusive evidence identifying the factors that affect the implementation and successful integration of mHealth into a health system. OBJECTIVE: The aim of this study is to examine the individual, organizational, and external level factors associated with the effective implementation of WelTel, an mHealth intervention designed to support outpatient medication adherence and engagement in care in Africa and North America. METHODS: We will adopt the Consolidated Framework for Implementation Research (CFIR) constructs for evaluation of mHealth implementation including a scoring and monitoring system. We will apply the adapted tool to identify facilitators and barriers to implementation of the WelTel mHealth intervention in order to determine how the technology platform is perceived, diffused, adapted, and used by different mHealth project teams and health system actors in Africa and North America. We will use a mixed-methods approach to quantitatively test whether the factors identified in the CFIR framework are associated with the successful uptake of the mHealth intervention toward implementation goals. We will triangulate these data through interviews and focus group discussion with project stakeholders, exploring factors associated with successful implementation and sustainment of these interventions. RESULTS: The development of the customized CFIR is finalized and currently is in pilot testing. The initial results of the use of the tool in those 13 implementations will be available in 2019. Continuous conference and peer- reviewed publications will be published in the coming years. CONCLUSIONS: The results of this study will provide an in-depth understanding of individual, organizational, and external level factors that influence the successful implementation of mHealth in different health systems and geographic contexts over time. Via the tool's unique scoring system connected to qualitative descriptors, these data will inform the most critical implementation targets and contribute to the tailoring of strategies that will assist the health system in overcoming barriers to implementation, and ultimately, improve treatment adherence and engagement in care. REGISTERED REPORT IDENTIFIER: RR1-10.2196/9633.
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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 | 0.098 | 0.060 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.052 | 0.011 |
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".