Typology and Ethical Considerations of Digital Health Promotion Tools for Youth in Sub-Saharan Africa: Review of Examples From Ghana, Kenya, and South Africa
Bibliographic record
Abstract
Background: Digital technologies for health promotion have proliferated over the past decade, with uptake increasing steadily among young people, including those in low- and middle-income countries (LMICs). Youth increasingly rely on digital tools for health information, and the early influence of this digital technology can have an impact throughout the lifespan. While there is a growing body of literature on the opportunities and challenges of digital health promotion (DHP) for young people, a gap remains in research that closely examines the characteristics of digital health strategies developed specifically for youth in LMICs. Objective: In this paper, we investigate and compare selected examples of DHP tools from 3 countries in Sub-Saharan Africa, namely Ghana, Kenya, and South Africa. Our aim is to create a multidimensional descriptive typology of DHP tools developed specifically to promote the health of adolescents and young adults in these countries. Methods: To select the tools, we conducted systematic internet-based searches using relevant keywords, incorporating the expertise of local professionals to ensure a thorough search. Included solutions originated from one of the 3 countries of focus and could take any number of forms such as apps, websites, chatbots, or social media initiatives. We thereafter deductively created a typology describing selected features of each tool, including the health area of focus, key stakeholders, type of service, and ethical values explicitly referenced within the tool. While such high-level features of interest were selected based on the existing literature in the field, the detailed descriptive categories were identified through an inductive analysis of the tools. Results: A total of 31 DHP tools were identified. Sexual and reproductive health was the most common health area of focus for DHP services, which were primarily funded and supported by local non-governmental organizations, foundations, and international organizations. The assessed tools were predominantly web-based and social media-based, with the overarching goal and core value of expanding health knowledge and offering access to health promotion services to young people. Conclusions: With sustained investment, DHP can improve the health of young people while relieving pressure on health care services. The areas of mental health, as well as substance use prevention and nutrition, stand out with clear potential for health gains through investment in DHP. Addressing ethical concerns such as privacy, transparency, equity, and inclusiveness is essential to the safety, usefulness, and fairness of DHP. To achieve the greatest benefit, local youth perspectives and priorities should be included in DHP development. Local initiatives have the potential to be the most agile, flexible, and relevant for the target audience of young people, with the overall goal of early intervention and greater health quality throughout the lifespan, and more efficient use of health care resources.
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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 | 0.028 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".