mHealth and Health Care Service Delivery in Africa: A Systematic Review
Bibliographic record
Abstract
We conducted a systematic review of studies on mHealth and health care services delivery that were carried out within Africa. Our search process was through MEDLINE, and then on PubMed, we searched key terms based on various keywords: “Whatsapp, health, Africa, Text messages, health impact, Africa, mHealth tools, Africa”. This was done in December of 2018. Only English written articles from journals indexed in Science Citation Index Expanded and Social Science Citation Index were incorporated in this review. In line with our inclusion criteria, only a total of 19 out of 155 studies were relevant. Inferences from these studies showed that mHealth tools are speedy and quality means for health care delivery in Africa. We also found out that there is less usage of internet devices in Africa as suspected. There is a serious need for improvement in the use of other online based mHealth tools as it was found that the use of Short Messaging Service (SMS) has been the nearly the sole mHealth intervention utilized in Africa. This, it is believed would foster better wider intervention and implementation of quality health outcomes in Africa, and other low and middle-income regions of the world.
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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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".