Needs for Technology-Enhanced Health Professions Education in Eastern and Southern Africa: Protocol for a Descriptive, Cross-Sectional Survey
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,036 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,033 | 0,006 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».