Mobile technology as a health literacy enabler in African rural areas: a literature review
Bibliographic record
Abstract
Abstract Background: Since the launch of mobile phones three decade ago, the latter have been used to support healthcare systems through various mobile health (m-health) applications. In recent years, multiple mobile phone-based health projects and applications have emerged. Despite the great enthusiasm around m-health, few studies have examined the use of cell phones to improve health literacy in Africa. This paper aims to review studies related to the contribution of mobile technologies in improving health literacy in rural areas of Africa. Methods: We performed a four-step systematic review to identify relevant publications: (1) Database selection, (2) Keyword search, (3) Study selection and (4) Data extraction. In addition, manual searching methods were used to find keywords related to m-health initiatives in Africa. Discussion: Our search resulted in the identification of 38 studies and initiatives related to health literacy and mobile technologies in Africa. However, few of these studies focused on health literacy and mobile technologies in rural areas of Africa. We also found that m-health initiatives to date have not been inclusive, with very few integrating local languages in the development of m-health solutions. Our findings thus point to various potential avenues to be investigated in the future.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".