How Self-Directed e-Learning Contributes to Training for Medical Licentiate Practitioners in Zambia: Evaluation of the Pilot Phase of a Mixed-Methods Study
Notice bibliographique
Résumé
BACKGROUND: Zambia faces a severe shortage of health workers, particularly in rural areas. To tackle this shortage, the Medical Licentiate program was initiated at Chainama College of Health Sciences in the capital, Lusaka, in 2002. The objective of the program was to alleviate the shortage of human resources in curative care. On-the-job training is conducted in decentralized teaching hospitals throughout Zambia. However, the program faces significant challenges such as shortages of senior medical instructors and learning materials. OBJECTIVE: Our aim was to address these challenges by introducing a self-directed, e-learning platform with an offline tablet as part of a collaborative blended-learning intervention to supplement local teaching and training. METHODS: The pilot phase of the e-learning platform was evaluated using a mixed-methods approach with a convergent parallel design. Various methods were employed to test the data's adequacy and potential for generating valid results. Methods included questionnaires according to the technology acceptance model and information system success model by DeLone and McLean, semistructured interviews, learner diaries, pretesting, the collection of usage data, exam results, demographics, and informal feedback. Outcome measures included usage, adoption, efficiency, acceptance, user-friendliness, and gained knowledge and skills. RESULTS: In total, 52 students and 17 medical instructors participated in the pilot evaluation. The questionnaire results showed a high acceptance of the technology (>80%) and high agreement (>75%) with the e-learning platform. Semistructured interview results showed an overall appreciation of the e-learning intervention, but the need for more e-learning materials. Respondents identified a need for multimedia materials that transfer skills such as medical procedure visualization and interactive exercises to practice procedural knowledge. The learning diaries identified the lack of specific learning materials and potential shortcomings of existing learning materials. However, students were satisfied with the current e-learning content. The majority of students used the e-learning platform offline on their tablets; online e-learning was underutilized. CONCLUSIONS: The pilot phase of the tablet-based e-learning platform to support the self-directed learning intervention was well received and appreciated by students and medical instructors of Chainama College of Health Sciences. E-learning for knowledge acquisition appears to be adequate and feasible for this low-resource educational environment. Our evaluation results guide the further development of the full implementation of the e-learning platform in this educational setting. E-learning materials should reflect curriculum requirements, and additional multimedia and interactive content is needed, as well as improved integration and active participation from medical instructors in the e-learning processes.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,047 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».