Simulation and mental health outcomes: a scoping review
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Notice bibliographique
Résumé
BACKGROUND: A scoping review was conducted in order to map and determine the gaps in literature on the impact of simulation as an educational approach to improve mental health care outcomes. As it became apparent that no literature existed on this topic, the study aimed to examine the educational impact of simulation on mental health education. METHODS: An established five-stage scoping methodology was used: (1) identification of the research question, (2) identification of relevant studies, (3) study selection, (4) charting the data and (5) collation, summarising and reporting of results. CINAHL, ProQuest, PubMed, MEDLINE, EMBASE and PsychINFO databases were searched. These databases were deemed to represent a majority of the literature while accommodating for the particular search strategy used for this review. Websites that provide grey literature were also searched for articles of relevance. RESULTS: A total of 48 articles were included in this review, with a considerable portion of studies conducted in the USA and UK. Others were conducted in an array of locations including Australia, Canada, Iran and Taiwan. Of the included articles, seven groups of simulation methods (including standardised patients, virtual reality and manikins as patients) were evident, with standardised patients being most prominent. CONCLUSIONS: Literature is lacking to evidence the benefit of simulation on mental health patient outcomes. However, the available literature suggests a variety of simulation-based education, and training methods are currently being used within mental healthcare education. The findings do suggest some methods of simulation, such as the use of standardised patients, are more commonly used in education and have been deemed as effective to assist in mental health education. As no article specifically examining the mental health outcomes of patients treated by health professionals taught by simulation was identified, the educational outcomes outlined in this paper may be used to inform further research, incorporating mental health patient outcomes.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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 découle