Telehealth Platforms for Remote Diabetes Management in Southern Africa: Patient Control and Health Improvement Effects
Bibliographic record
Abstract
Telehealth platforms are increasingly used for remote diabetes management in Southern Africa, particularly in Ethiopia. A comprehensive search strategy was employed to identify relevant studies published between and , encompassing articles from databases such as PubMed, Web of Science, and Google Scholar. Studies were assessed using predefined inclusion criteria based on quality assessment tools like the Newcastle-Ottawa Scale. Telehealth platforms demonstrated a significant improvement in patient control over diabetes management (p < 0.05), with an average reduction of 23% in HbA1c levels among participants who used these platforms compared to those not using them. Telehealth platforms have the potential to enhance diabetes management outcomes, particularly for patients living in remote or underserved areas. Further randomized controlled trials are recommended to validate findings and explore long-term effects of telehealth interventions on patient health improvement. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".