A framework for community ownership of a text messaging programme to improve adherence to antiretroviral therapy and client-provider communication: a mixed methods study
Bibliographic record
Abstract
BACKGROUND: Mobile phone text messaging has been shown to improve adherence to antiretroviral therapy and to improve communication between patients and health care workers. It is unclear which strategies are most appropriate for scaling up text messaging programmes. We sought to investigate acceptability and readiness for ownership (community members designing, sending and receiving text messages) of a text message programme among a community of clients living with human immunodeficiency virus (HIV) in Yaoundé, Cameroon and to develop a framework for implementation. METHODS: We used the mixed-methods sequential exploratory design. In the qualitative strand we conducted 7 focus group discussions (57 participants) to elicit themes related to acceptability and readiness. In the quantitative strand we explored the generalizability of these themes in a survey of 420 clients. Qualitative and quantitative data were merged to generate meta-inferences. RESULTS: Both qualitative and quantitative strands showed high levels of acceptability and readiness despite low rates of participation in other community-led projects. In the qualitative strand, compared to the quantitative strand, more potential service users were willing to pay for a text messaging service, preferred participation of health personnel in managing the project and preferred that the project be based in the hospital rather than in the community. Some of the limitations identified to implementing a community-owned project were lack of management skills in the community, financial, technical and literacy challenges. Participants who were willing to pay were more likely to find the project acceptable and expressed positive feelings about community readiness to own a text messaging project. CONCLUSION: Community ownership of a text messaging programme is acceptable to the community of clients at the Yaoundé Central Hospital. Our framework for implementation includes components for community members who take on roles as services users (demonstrating clear benefits, allowing a trial period and ensuring high levels of confidentiality) or service providers (training in project management and securing sustainable funding). Such a project can be evaluated using participation rate, clinical outcomes, satisfaction with the service, cost and feedback from users.
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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 | 0.216 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| 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".