Implementation and impact of mobile health (mHealth) in the management of diabetes mellitus in Africa: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: The WHO has proposed the concept of mobile health (mHealth) to support healthcare systems delivery worldwide. mHealth basically involves the use of Information and Communication Technology for healthcare provision or delivery services. Africa has seen a remarkable increase in mobile phone availability and usage in the last decade. The incidence and prevalence of diabetes mellitus (DM) in Africa have also been on the increase in the last decade, in sharp contrast to an ailing healthcare system. We aim to review the extent of implementation of mHealth in the management of DM in Africa, and estimate its impact in helping patients achieve desired glycaemic target, sustain control and prevent complications in the past decade. METHODS AND ANALYSIS: Studies assessing the utilisation of mhealth in the management of patients with DM in Africa will be considered based on the PICO method: Population, Intervention, Comparator, and Outcomes. Medline, PubMed, SCOPUS and the Pan African Clinical Trials Registry, among others will be searched. Two authors independent of each other shall screen titles and abstracts retrieved using the search strategy, retrieve the full text articles and assess them for eligibility and extract data. A third reviewing author will be brought in to resolve any disagreement between the two authors by discussion. The 'Cochrane Collaboration Risk of Bias Tool' will be used to assess the quality of included studies. A narrative synthesis of extracted data and, where the characteristics of the eligible studies permit, a meta-analysis (which will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines) will be done. ETHICS AND DISSEMINATION: No ethical approval will be required since only published data will be used. Dissemination of results will be through peer reviewed publication and conference presentation. PROSPERO REGISTRATION NUMBER: CRD42021218674.
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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 | 0.058 | 0.054 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.015 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.056 | 0.006 |
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".