Stakeholder perceptions about the establishment of medical simulation-based learning at a university in a low resource setting: a qualitative study in Uganda
Notice bibliographique
Résumé
BACKGROUND: Simulation based learning (SBL) is a technique where teachers recreate "real life" clinical experiences for health care teams for purposes of gaining clinical skills in a safe environment. There is evidence that SBL is superior to the traditional clinical teaching methods for acquisition of clinical skills. Although it is well established in resource rich settings, there is limited experience in resource limited settings and there is uncertainty regarding how SBL will be perceived among the stakeholders in medical education. As part of the steps leading to implementation of a SBL program at a university in Uganda, we sought to describe the perceptions of various stakeholders regarding the introduction of SBL methodology into learning at a medical school in Uganda. METHODS: We conducted a formative qualitative assessment using key informant interviews (KIIs) among faculty members and university administrators and focus group discussions (FGDs) among medical and nursing students at Mbarara University of Science and Technology. Data were collected till saturation point and were transcribed and analyzed manually using open and axial coding approaches to develop themes. RESULTS: We conducted seven KIIs and three FGDs. Overall, findings were categorized into five broad themes: 1. Motivation to adopt simulation-based learning 2. Prior experience and understanding of simulation-based education 3. Outcomes arising from introduction of medical simulation 4. Drawbacks to establishment of medical simulation; and 5. Potential remedies to the drawbacks. Overall, our data show there was significant buy-in from the institution for SBL, stakeholders were optimistic about the prospects of having a new method of teaching, which they perceived as modern to complement the traditional methods. There was significant knowledge but very limited prior experience of medical simulation. Also, there was some concern regarding how students and faculty would embrace training on lifeless objects, the human resources needed and sustainability of simulation-based learning in the absence of external funding. CONCLUSION: Stakeholders perceive SBL positively and are likely to embrace the learning methods. Concerns about human resource needs and sustainability need to be addressed to ensure acceptability.
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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 | 0,003 | 0,014 |
| 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».