Digital vs. conventional instructor-led midwifery training in Benue State, Nigeria: a randomized non-inferiority trial
Notice bibliographique
Résumé
Background Many health education institutions in African countries such as Nigeria face increasing enrolment of students but lack an adequate number of instructors to train them. If digital learning can be demonstrated effective in augmenting knowledge and skills, this approach could help fill this gap and improve education efficiency. A needs assessment in two Nigerian midwifery schools confirmed that digital learning would be feasible and welcomed. In this study, the Midwifery Active Digitization Empowerment Initiative (MADE-I) program was tested to determine if digital delivery of the Fundamental Interventions, Referral and Safe Transfer (FIRST) course is at least equally effective for training midwifery students compared to conventional small-group delivery. Methods A non-inferiority randomized controlled trial design was used, enrolling 130 s-year students from 2 midwifery schools in Benue State, Nigeria. Students were randomly assigned into six cohorts. Each cohort received half of the course on a Learning Management Platform on their mobile phones, the other half through standard small-group teaching. Students’ knowledge, thinking, and technical skills were assessed using a pre-test, post-test, Objective Structured Clinical Exam (OSCE), and daily modular quizzes. The data was analyzed using the difference-in-difference method. Results The study revealed that post-intervention student knowledge and thinking skills did not significantly differ between digital learning (75.26%) and small-group learning arms of the trial (75.02%, p = 0.404). Student knowledge improved significantly compared to the pre-test in both groups (by 25.03 points in the digital arm, 26.39, in small-group). Some differences were observed between digital and small-group learning in disaggregated analysis by specific module and midwifery school. Although there was a trend toward small-group learning of technical skills being more effective than digital learning, no significant differences between groups were observed in the post-intervention OSCE. Students in both groups learned equally well regardless of age, gender, and midwifery school entrance exam score. Conclusion Digital learning is as effective as small-group learning, for midwifery trainees, in augmenting knowledge, thinking, and technical skills addressed in the FIRST course, and have lighter human resource requirements, an important consideration especially in LMIC. However, similar assessments would be needed to assess effectiveness for other digitally delivered clinical education programs.
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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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».