A Digital Sexual Health Education Web Application for Resource-Poor Regions in Kenya: Implementation-Oriented Case Study Using the Intercultural Research Model
Bibliographic record
Abstract
BACKGROUND: Developing a digital educational application focused on sexual health education necessitates a framework that integrates cultural considerations effectively. Drawing from previous research, we identified the problem and essential requirements to incorporate cultural insights into the development of a solution. OBJECTIVE: This study aims to explore the Solution Room of the self-established Intercultural Research Model, with a focus on creating a reusable framework for developing and implementing a widely accessible digital educational tool for sexual health. The study centers on advancing from a low-fidelity prototype (She!Masomo) to a high-fidelity prototype (We!Masomo), while evaluating its system usability through differentiation. This research contributes to the pursuit of Sustainable Development Goals 3, 4, and 5. METHODS: The research methodology is anchored in the Solution Room of the self-expanded Intercultural Research Model, which integrates cultural considerations. It uses a multimethod, user-centered design thinking approach, focusing on extensive human involvement for the open web-based application. This includes gathering self-assessed textual user feedback, conducting a System Usability Scale (SUS) analysis, and conducting 4 face-to-face semistructured expert interviews, following COREQ (Consolidated Criteria for Reporting Qualitative Research) guidelines. RESULTS: Based on the identified limitations of the low-fidelity prototype, She!Masomo (SUS score 67), which were highlighted through textual user feedback (63/77) and prototype feature comparisons, iterative development and improvement were implemented. This process led to the creation of an enhanced high-fidelity prototype (We!Masomo). The improved effectiveness of the enhanced prototype was evaluated using the qualitative SUS analysis (82/90), resulting in a favorable score of 77.3, compared with the previous SUS score of 67 for the low-fidelity prototype. Highlighting the importance of accessible digital educational tools, this study conducted 4 expert interviews (4/4) and reported e-survey results following the CHERRIES (Checklist for Reporting Results of Internet E-Surveys) guideline. The digital educational platform, We!Masomo, is specifically designed to promote universal and inclusive free access to information. Therefore, the developed high-fidelity prototype was implemented in Kenya. CONCLUSIONS: The primary outcome of this research provides a comprehensive exploration of utilizing a case study methodology to advance the development of digital educational web tools, particularly focusing on cultural sensitivity and sensitive educational subjects. It offers critical insights for effectively introducing such tools in regions with limited resources. Nonetheless, it is crucial to emphasize that the findings underscore the importance of integrating culture-specific components during the design phase. This highlights the necessity of conducting a thorough requirement engineering analysis and developing a low-fidelity prototype, followed by an SUS analysis. These measures are particularly critical when disseminating sensitive information, such as sexual health, through digital platforms. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12905-023-02839-6.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".