Evaluating the Instructional Design and Effect on Knowledge, Teamwork, and Skills of Technology-Enhanced Simulation-Based Training in Obstetrics in Uganda: Stepped-Wedge Cluster Randomized Trial
Notice bibliographique
Résumé
BACKGROUND: Simulation-based training is a common strategy for improving the quality of facility-based maternity services and is often evaluated using Kirkpatrick's theoretical model. The results on the Kirkpatrick levels are closely related to the quality of the instructional design of a training program. The instructional design is generally defined as the "set of prescriptions for teaching methods to improve the quality of instruction with a goal of optimizing learning outcomes." OBJECTIVE: The aim of this study is to evaluate the instructional design of a technology-enhanced simulation-based training in obstetrics, the reaction of participants, and the effect on knowledge, teamwork, and skills in a low-income country. METHODS: A stepped-wedge cluster randomized trial was performed in a university hospital in Kampala, Uganda, with an annual delivery volume of over 31,000. In November 2014, a medical simulation center was installed with a full-body birthing simulator (Noelle S550, Gaumard Scientific), an interactive neonate (Simon S102 Newborn CPR Simulator, Gaumard Scientific), and an audio and video recording system. Twelve local obstetricians were trained and certified as medical simulation trainers. From 2014 to 2016, training was provided to 57 residents in groups of 6 to 9 students. Descriptive statistics were calculated for ten instructional design features of the training course measured by the 42-item ID-SIM (Instructional Design of a Simulation Improved by Monitoring). The Wilcoxon signed rank test was conducted to investigate the differences in scores on knowledge, the Clinical Teamwork Scale, and medical technical skills. RESULTS: The mean scores on the ten instructional design features ranged from 54.9 (95% CI 48.5-61.3) to 84.3 (95% CI 80.9-87.6) out of 100. The highest mean score was given on the feature feedback and the lowest scores on repetitive practice and controlled environment. The overall score for the training day was 92.8 out of 100 (95% CI 89.5-96.1). Knowledge improved significantly, with a test score of 63.4% (95% CI 60.7-66.1) before and 78.9% (95% CI 76.8-81.1) after the training (P<.001). The overall score on the 10-point Clinical Teamwork Scale was 6.0 (95% CI 4.4-7.6) before and 5.9 (95% CI 4.5-7.2) after the training (P=.78). Medical technical skills were scored at 55.5% (95% CI 47.2-63.8) before and 65.6% (95% CI 56.5-74.7) after training (P=.08). CONCLUSIONS: Most instructional design features of a technology-enhanced simulation-based training in obstetrics in a low-income country were scored high, although intervals were large. The overall score for the training day was high, and knowledge did improve after the training program, but no changes in teamwork and (most) medical technical skills were found. The lowest-scored instructional design features may be improved to achieve further learning aims. TRIAL REGISTRATION: ISRCTN Registry ISRCTN98617255; http://www.isrctn.com/ISRCTN98617255. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12884-020-03050-3.
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 | 0,016 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 ».