Evaluation of the implementation process of the mobile health platform ‘WelTel’ in six sites in East Africa and Canada using the modified consolidated framework for implementation research (mCFIR)
Bibliographic record
Abstract
BACKGROUND: Health systems globally are investing in integrating secure messaging platforms for virtual care in clinical practice. Implementation science is essential for adoption, scale-up, spread and maintenance of complex evidence-based solutions in clinics with evolving priorities. In response, the mobile Health (mHealth) Research Group modified the existing consolidated framework for implementation research (CFIR) to evaluate implementation of virtual health tools in clinical settings. WelTel® is an evidence-based digital health platform widely deployed in various geographical and health contexts. The objective is to identify the facilitators and barriers for implementing WelTel and to assess the application of the mCFIR tool in facilitating focus groups in different geographical and health settings. METHODS: Both qualitative and descriptive quantitative approaches were employed. Six mCFIR sessions were held in three countries with 51 key stakeholders. The mCFIR tool consists of 5 Domains and 25 constructs and was distributed through Qualtrics Experience Management (XM). "Performance" and "Importance" scores were valued on a scale of 0 to 10 (Mean ± SD). Descriptive analysis was conducted using R computing software. NVivo 12 Pro software was used to analyze mCFIR responses and to generate themes from the participants' input. RESULTS: We observed a parallel trend in the scores of Importance and Performance. Of the five Domains, Domain 4 (End-user Characteristics) and Domain 3 (Inner Settings) scored highest in Importance (8.9 ± 0.5 and 8.6 ± 0.6, respectively) and Performance (7.6 ± 0.7 and 7.2 ± 1.3, respectively) for all sites. Domain 2 (Outer Setting) scored the lowest in both Importance and Performance for all sites (7.6 ± 0.4 and 5.6 ± 1.8). The thematic analysis produced the following themes: for areas of strengths, the themes brought up were timely diagnosis and response, cost-effectiveness, and user-friendliness. As for areas for improvement, the themes discussed were training, phone accessibility, stakeholder engagement, and literacy. CONCLUSION: The mCFIR tool allowed for a comprehensive understanding of the barriers and facilitators to the implementation, reach, and scale-up of digital health tools. Amongst several important findings, we observed the value of bringing the perspectives of both end users (HCPs and patients) to the table across Domains. TRIAL REGISTRATION: NCT02603536 - November 11, 2015: WelTelOAKTREE: Text Messaging to Support Patients With HIV/AIDS in British Columbia (WelTelOAKTREE). NCT01549457 - March 9, 2012: TB mHealth Study-Use of Cell Phones to Improve Compliance in Patients on LTBI Treatment.
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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 | 0.113 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".