Considerations in using text messages to improve adherence to highly active antiretroviral therapy: a qualitative study among clients in Yaoundé, Cameroon
Bibliographic record
Abstract
Lawrence Mbuagbaw1,2, Renée Cécile Bonono-Momnougui1, Lehana Thabane2,31Centre for the Development of Best Practices in Health (CDBPH), Yaoundé Central Hospital, Yaoundé, Cameroon; 2Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada; 3Biostatistics Unit, Father Sean O'Sullivan Research Centre, St Joseph's Healthcare, Hamilton, Ontario, CanadaAbstract: Poor adherence to highly active antiretroviral therapy (HAART) is a major hindrance to the reduction of mortality and morbidity due to HIV. This qualitative study used focus groups to explore the views and experiences of HIV patients on HAART with adherence reminders, especially the text message (SMS [short message service]). The ethnographic data obtained were used to design a clinical trial to assess the effect of motivational text messages versus usual care to enhance adherence to HAART among HIV patients in Yaoundé, Cameroon. Participants appreciated the idea of a timely SMS reminder, and cited the physician as a role model. They expressed concerns about privacy. Long-term life goals were a motivating factor to adhere. Overall, text messaging was viewed positively as a tool with a dual function of reminder and motivator. Messages coming from the attending physician may have a stronger impact. Trials investigating the use of text messages to improve adherence to HAART need to consider the content and timing of SMS, taking into account technical challenges and privacy.Keywords: focus groups, adherence, highly active antiretroviral therapy (HAART), text message, short message service (SMS), human immunodeficiency virus (HIV)
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".