Mobile health applications for HIV prevention and care in Africa
Bibliographic record
Abstract
PURPOSE OF REVIEW: More people have mobile phones in Africa than at any point in history. Mobile health (m-health), the use of mobile phones to support the delivery of health services, has expanded in recent years. Several models have been proposed for conceptualizing m-health in the fields of maternal-child health and chronic diseases. We conducted a literature review of m-health interventions for HIV prevention and care in African countries and present the findings in the context of a simplified framework. RECENT FINDINGS: Our review identified applications of m-health for HIV prevention and care categorized by the following three themes: patient-care focused applications, such as health behavior change, health system-focused applications, such as reporting and data collection, and population health-focused applications, including HIV awareness and testing campaigns. SUMMARY: The potential for m-health in Africa is numerous and should not be limited only to direct patient-care focused applications. Although the use of smart phone technology is on the rise in Africa, text messaging remains the primary mode of delivering m-health interventions. The rate at which mobile phone technologies are being adopted may outpace the rate of evaluation. Other methods of evaluation should be considered beyond only randomized-controlled trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".