In situ simulation and its different applications in healthcare: an integrative review
Notice bibliographique
Résumé
Abstract Introduction: The in situ simulation (ISS) consists of a training technique that takes place in the real workplace as a relevant method to promote environmental fidelity in the simulated scenario. Objective: To verify the use of the ISS in the world, to understand its applicability in healthcare. Method: This is an integrative review, which used the following guiding question: How has in situ simulation been used by health professionals? Searches were carried out in the PubMed, SciELO, LILACS and Web of Science databases, with different combinations of the following descriptors: in situ simulation, health and medicine (in Portuguese, English and Spanish) and the Boolean operators AND and OR using a temporal filter from 2012 to 2021. A total of 358 articles were found and the inclusion and exclusion criteria were applied, following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), and also with an independent peer review, using Rayyan, leaving 190 articles for this review. Results: The results showed that the United States has the absolute majority of productions (97/51%), followed by Canada, but with a large numerical difference (18/9.5%). Most of the works are written in English (184/96.8%), are quasi-experimental studies (97/51%), and have multidisciplinary teams as the target audience (155/81.6%). The articles have 11,315 participants and 2,268 simulation interventions. The main ISS scenarios were the urgent and emergency sectors (114/60%), followed by the ICU (17/9%), delivery room (16/8.42%) and surgical center (13/6.84%). The most frequently studied topics were CPR (27/14.21%), COVID-19 (21/11%), childbirth complications (13/6.8%) and trauma (11/5.8%). Discussion: The pointed-out advantages include the opportunity for professional updating with the acquisition of knowledge, skills and competencies, in an environment close to the real thing and at low cost, as it does not depend on expensive simulation centers. Conclusion: In situ simulation has been used by health professionals worldwide, as a health education strategy, with good results for learning and training at different moments of professional training, with improved care and low cost. There is still much to expand in relation to the use of ISS, especially in Brazil, in the publication of studies and experience reports on this approach.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| 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) | 0,000 | 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 ».