An Implementation Strategy to Expand Mobile Health Use in HIV Care Settings: Rapid Evaluation Study Using the Consolidated Framework for Implementation Research
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) apps can provide support to people living with a chronic disease by offering resources for communication, self-management, and social support. PositiveLinks (PL) is a clinic-deployed mHealth app designed to improve the health of people with HIV. In a pilot study, PL users experienced considerable improvements in care engagement and viral load suppression. To promote its expansion to other HIV clinics, we developed an implementation strategy consisting of training resources and on-demand program support. OBJECTIVE: The objective of our study was to conduct an interim analysis of the barriers and facilitators to PL implementation at early adopting sites to guide optimization of our implementation strategy. METHODS: Semistructured interviews with stakeholders at PL expansion sites were conducted. Analysis of interviews identified facilitators and barriers that were mapped to 22 constructs of the Consolidated Framework for Implementation Research (CFIR). The purpose of the analysis was to identify the facilitators and barriers to PL implementation in order to adapt the PL implementation strategy. Four Ryan White HIV clinics were included. Interviews were conducted with one health care provider, two clinic managers, and five individuals who coordinated site PL activities. RESULTS: Ten common facilitators and eight common barriers were identified. Facilitators to PL implementation included PL's fit with patient and clinic needs, PL training resources, and sites' early engagement with their information technology personnel. Most barriers were specific to mHealth, including access to Wi-Fi networks, maintaining patient smartphone access, patient privacy concerns, and lack of clarity on how to obtain approvals for mHealth use. CONCLUSIONS: The CFIR is a useful framework for evaluating mHealth interventions. Although PL training resources were viewed favorably, we identified important barriers to PL implementation in a sample of Ryan White clinics. This enabled our team to expand guidance on identifying information technology stakeholders and procuring and managing mobile resources. Ongoing evaluation results continue to inform improvements to the PL implementation strategy, facilitating PL access for future expansion sites.
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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 | 0.239 | 0.170 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".