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Enregistrement W2306704421 · doi:10.5116/ijme.56ed.1060

Identification tags and learners’ situational awareness during high-fidelity simulation

2016· article· en· W2306704421 sur OpenAlexaffabout
Issam Tanoubi, Marie‐Ève Bélanger, Leonardo Georgescu, Roger Perron, Jean‐François Germain, Arnaud Robitaille, Pierre Drolet

Notice bibliographique

RevueInternational Journal of Medical Education · 2016
Typearticle
Langueen
DomaineMedicine
ThématiqueSimulation-Based Education in Healthcare
Établissements canadiensUniversité du Québec à MontréalUniversité de MontréalMontreal Clinical Research Institute
Organismes subventionnairesnon disponible
Mots-clésSituation awarenessIdentification (biology)Computer scienceFidelityHigh fidelityPsychologySituational ethicsData scienceSocial psychologyEngineeringBiology

Résumé

récupéré en direct d'OpenAlex

Simulation-based medical education is an integral part of the curriculum of many specialties. Simulation allows participants to develop and practice technical skills useful to the management of rare and urgent clinical situations in a safe and supportive learning environment.1 Several points are deemed essential to the promotion of optimal learning during simulation. One, which was emphasized by Issenberg and associates, stated that acquiring experience in medicine was governed by the learners’ commitment to a simulation’s realistic environment.2 Obtaining and maintaining the commitment of participants in a high-fidelity simulation scenario is then essential for a better learning experience. One way to foster a learner’s commitment toward a simulated environment is to help the learner gain a greater awareness of the environment’s characteristics. With better situational awareness, participants can more easily understand the various elements and the complexity of the environment, and then anticipate the human and material resources that might be needed. This could allow students to engage in better clinical decision making. Situational awareness has been measured by quantitative scales in studies conducted in the aviation and maritime fields, but additional adjustment and validation of these scales are still required.3,4 Moreover, specific factors affecting the situational awareness remain unstudied. Here, we undertook this research to assess whether wearing badges or name tags stating each participant’s function and posting a sign to clearly inform students of the scenario’s location during the high-fidelity simulation could lead to greater awareness of each learner’s role and contribute to their commitment. We hypothesized that using badges and formally identifying the location of the scenario would enable participants to feel more committed and to better identify everyone’s role and where the action was taking place. Evaluating the importance of the name tags and place identification We used a pretest-posttest design to achieve our study goals. Our study took place within a planned simulation training course for 25 anesthesiology residents on the management of critical anesthesia situations from April 30, 2014, to June 11, 2014. Study coordinators assigned 4 to 5 participants to each of the 6 overall simulation sessions scheduled. Each session was 4 hours long and included an introduction, followed by two high-fidelity simulated scenarios with debriefing. The high-fidelity simulation was operationalized through high equipment fidelity (functionality and responsiveness of patients, manikins, and medical instruments), high environment fidelity (real world overload demands), and high psychologic fidelity (maintaining the natural ‘‘flow’’ of a clinical scenario and participant’s immersion within the scenario).5 One scenario was about a patient who had a massive amniotic fluid embolism that occurred after a complicated delivery and leading to a maternal cardiac arrest, and the alternative scenario was of a patient with postoperative malignant hyperthermia. The order of the two scenarios was randomized for each session. During the first scenario of the session, participants did not wear badges, and no sign that formally identified the mock location was allowed. In the second scenario, name badges and identifying signs were used systematically. At the end of each scenario, all residents completed, using an audience response system (Turning Point, Ontario, Canada, Turning Technologies), a 7-question survey  (7-point Likert scale) to evaluate their situational awareness and their level of commitment. An evaluator, blinded to the randomization sequence, listened to the soundtrack only of the audio-video recording of the simulated scenarios in search of indicators suggesting a lack of situational awareness. We noted no differences between the groups regarding their consciousness of the location, or of the various roles played by the participants. We found that the resident’s engagement was also similar in both groups. The number of indicators of poor situational awareness, noted by the blinded evaluator during audio review of the scenario’s video recording was not significantly different between groups.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,176
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,029
Tête enseignante GPT0,424
Écart entre enseignants0,395 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2016
Routes d'admission2
Résumé présentoui

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