Using serious mobile games to improve health literacy in rural Sub-Saharan Africa: A literature review
Bibliographic record
Abstract
The African gaming industry is beginning to flourish as a result of a rise in the availability of inexpensive phones and the number of mobile phone subscribers. It has enabled the development and implementation of mobile serious games to promote healthy behavior change in rural communities. This paper examines the use of mobile serious games in healthcare education, with a particular focus on those designed to increase health literacy in rural Africa. Identifying and addressing the design challenges and issues faced by people living in rural African communities through the use of persuasive mobile games can promote behavior change among these underserved communities. We used PubMed, Scopus, Google Scholar and manual search to identify relevant studies published from 2011 to July 2021. The literature review highlights how the identified challenges affect the implementation of persuasive strategies, suggests design solutions for overcoming them, and discusses how persuasive games can be tailored to suit the target rural African populations. Some of the identified challenges are technical in nature (e.g., access to electricity and internet connectivity), while others are not (e.g., language diversity and low literacy). As the number of serious games for healthcare education and awareness continues to increase, it is essential for the successful implementation of inclusive mobile health technologies in rural Africa to identify and address the specific challenges faced by underserved populations such as rural African communities.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".