Enabling Access to Medical and Health Education in Rwanda Using Mobile Technology: Needs Assessment for the Development of Mobile Medical Educator Apps
Bibliographic record
Abstract
BACKGROUND: Lack of access to health and medical education resources for doctors in the developing world is a serious global health problem. In Rwanda, with a population of 11 million, there is only one medical school, hence a shortage in well-trained medical staff. The growth of interactive health technologies has played a role in the improvement of health care in developed countries and has offered alternative ways to offer continuous medical education while improving patient's care. However, low and middle-income countries (LMIC) like Rwanda have struggled to implement medical education technologies adapted to local settings in medical practice and continuing education. Developing a user-centered mobile computing approach for medical and health education programs has potential to bring continuous medical education to doctors in rural and urban areas of Rwanda and influence patient care outcomes. OBJECTIVE: The aim of this study is to determine user requirements, currently available resources, and perspectives for potential medical education technologies in Rwanda. METHODS: Information baseline and needs assessments data collection were conducted in all 44 district hospitals (DHs) throughout Rwanda. The research team collected qualitative data through interviews with 16 general practitioners working across Rwanda and 97 self-administered online questionnaires for rural areas. Data were collected and analyzed to address two key questions: (1) what are the currently available tools for the use of mobile-based technology for medical education in Rwanda, and (2) what are user's requirements for the creation of a mobile medical education technology in Rwanda? RESULTS: General practitioners from different hospitals highlighted that none of the available technologies avail local resources such as the Ministry of Health (MOH) clinical treatment guidelines. Considering the number of patients that doctors see in Rwanda, an average of 32 patients per day, there is need for a locally adapted mobile education app that utilizes specific Rwandan medical education resources. Based on our results, we propose a mobile medical education app that could provide many benefits such as rapid decision making with lower error rates, increasing the quality of data management and accessibility, and improving practice efficiency and knowledge. In areas where Internet access is limited, the proposed mobile medical education app would need to run on a mobile device without Internet access. CONCLUSIONS: A user-centered design approach was adopted, starting with a needs assessment with representative end users, which provided recommendations for the development of a mobile medical education app specific to Rwanda. Specific app features were identified through the needs assessment and it was evident that there will be future benefits to ongoing incorporation of user-centered design methods to better inform the software development and improve its usability. Results of the user-centered design reported here can inform other medical education technology developments in LMIC to ensure that technologies developed are usable by all stakeholders.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".