Needs for Technology-Enhanced Health Professions Education in Eastern and Southern Africa: Protocol for a Descriptive, Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND: The use of technology in its various forms has long been a feature of the education and training of health professionals in the industrialized world. As a result of the COVID-19 pandemic, health professions education institutions suddenly adopted "emergency remote teaching," and this experience exposed the vulnerabilities of countries in Eastern and Southern Africa regarding modes of teaching and learning. In this region, the needs to migrate to effective technology-enhanced learning are not explicit. OBJECTIVE: The main objective of this study is to assess the needs for technology-enhanced health professions education in Eastern and Southern Africa. This will lead to the development of a tool reflecting different technologies used in health professions education. The tool will be used by educators to identify and bridge gaps in their use of technology in health professions education. METHODS: This will be a descriptive, cross-sectional survey, and data will be collected from medical and nursing programs at the bachelor's degree level recognized by national professional bodies or government structures offered at tertiary institutions in countries in Eastern and Southern Africa. The substitution, augmentation, modification, redefinition (SAMR) model underpins our study and serves as an organizing framework for the different types of technology in current use in the institutions under study. The SAMR model is a tool that provides guidance in describing and categorizing uses of educational technology in the classroom. The model is intended to guide educators to enhance their teaching and learning through the adoption, adaptation, or transformation of educational approaches using technology. To obtain the purposive sample, a person from each program who is well-acquainted with the program will identify staff members and students who represent the totality of those populations fairly. Quantitative data were analyzed descriptively for each program. Data were then organized according to the SAMR framework to portray the types of technology in use and challenges encountered. RESULTS: This research was funded in October 2023, and the first institutional review board approval was obtained in April 2024. Data collection began in September 2024 and ended in November 2024. Since this was a multi-institution study, we envisaged multiphase data analysis, which was completed in mid-December 2024. As of August 2025, the manuscript is under peer review for publication of the results. CONCLUSIONS: The study will reveal gaps in the use of technology, and this will lead to the identification of needs for enhancing technology in health professions education. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/67331.
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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 | 0.036 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".