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Enregistrement W2312953231 · doi:10.1097/01.sih.0000441589.28976.85

Board 337 - Research Abstract Simulation with Standardized Patients in Healthcare

2013· article· en· W2312953231 sur OpenAlexaff
Émilie Gosselin, Stéphan Lavoie, Isabelle Ledoux, Patricia Bourgault

Notice bibliographique

RevueSimulation in Healthcare The Journal of the Society for Simulation in Healthcare · 2013
Typearticle
Langueen
DomaineMedicine
ThématiqueSimulation-Based Education in Healthcare
Établissements canadiensUniversité de Sherbrooke
Organismes subventionnairesnon disponible
Mots-clésCINAHLReliability (semiconductor)Health careMEDLINEComputer scienceValidityProcess (computing)Medical educationPsychologyMedicineNursingPsychometricsPsychological interventionClinical psychology

Résumé

récupéré en direct d'OpenAlex

Introduction/Background Simulation is a relatively new teaching tool used to help healthcare students practice various assessments and skills in a safe environment.1–3 This innovative method can develop the process of care and, thus, improve patient outcome and safety.4 In research, simulation overcomes some ethical and methodological issues, in addition to allowing control over confounding variables.5 Therefore, it is important to develop standardized clinical simulations in order to facilitate education, clinical practice and research in nursing. At the development stage, particular attention should be given to achieve good reliability and validity throughout the evaluations.6–8 A question emerges: How can we improve the reliability and validity of simulations with standardized patients? An exhaustive literature review of various methodologies of simulations with standardized patients is presented. Methods A literature review was conducted in CINAHL, MEDLINE and PubMed to examine the available research findings related to methodologies used for conducting standardized simulations. The main keywords used to search these databases were “simulation,” “validity,” “reliability,” “clinical,” “standardized” and “development.” The literature review screened for papers in English or French written between 1993 and 2013. Articles were selected for their relevancy to the subject. Results Over 500 article titles were reviewed and among them, close to 100 abstracts have been read. Overall, 56 papers were included in the literature review. Some simple Methods help enhance the reliability and validity at each step of the process, from developing a new scenario to using the finished standardized simulation with participants. There are four main elements on which it is possible to act: the creation of the scenario, the standardized patient, the flow of the simulation and the simulation environment. The scenario must be precise and every eventuality should be considered with a different path to follow. It is important to select a realistic situation customized to the participants’ learning needs and level. An expert committee including simulation and clinical setting experts increases the validity of the scenario. The selection of a standardized patient is really important for reliability and validity. He or she should receive training or be familiar with the clinical setting to be able to reproduce the same scenario with precision. If possible, the same actor should be used with all participants to maintain a level of consistency over several sessions. The standardized patient should be able to review the scenario before simulation sessions. There should be a briefing before each session, including a tour of the simulation room. The presence of a facilitator in the room ensures rigorous monitoring of the simulation flow. The simulation room should reproduce reality as faithfully as possible, with attention to details to ensure the validity of the simulation. It must feel like a safe but real environment to the participants for them to perform as they would in a real clinical setting. Finally, a pretest with a sufficient number of participants allows to experiment each of these elements and to make adjustments if necessary. Conclusion Simulations with standardized patients are a useful teaching, practice and research tool. Paying attention to the psychometric properties of the simulation helps make them more lifelike and facilitates the transfer of new practical knowledge. Some simple Methods can be put forward to promote a good reliability and validity by controlling the development of the scenario, the standardized patient, the flow of the simulation, and the simulation environment. References 1. Bambini D, Washburn J, Perkins R: Outcomes of Clinical Simulation for Novice Nursing Students: Communication, Confidence, Clinical Judgement. Nursing Education Research 2009; 30:79–82. 2. Mavis BE, Ogle KS, Lovell KL, Madden LM: Medical students as standardized patients to assess interviewing skills for pain evaluation. Medical Education 2002; 36:135–140. 3. Murray D, Boulet J, Ziv A, Woodhouse J, Kras J, McAllister J: An acute care skills evaluation for graduating medical students: a pilot study using clinical simulation. Medical Education 2002; 36:833–841. 4. Griswold S, Ponnuru S, Nishisaki A, Szyld D, Davenport M, Deutsh ES, Nadkami V: The Emerging Role of Simulation Education to Achieve Patient Safety. Translating Deliberate Practice and Debriefing to Save Lives. Pediatric Clinics of North America 2012; 59:1329–1340. 5. Gosselin E, Bourgault P, Lavoie S, Coleman RM, Méziat-Burdin A: Development and validation of an observation tool for the assessment of nursing pain management practices in intensive care unit in a standardized clinical simulation setting. Pain Management Nursing [in press]. 6. Grady JL, Rosemary GK, Trusty CE, Entin EB, Entin EE, Brunye TT: Learning Nursing Procedures: The Influence of Simulator Fidelity and Student Gender on Teaching Effectiveness. Journal of Nursing Education 2008; 47:403–8. 7. Howley L, Szauter K, Perkowski L, Clifton M, McNaughton N: Quality of standardized patient research reports in the medical education literature review and recommendations. Medical Education 2008; 42:350–8. 8. Issenberg SB, McGaghie WC, Petrusa ER, Lee Gordon D, Scalese RJ: Features and uses of high-fidelity medical simulation that lead to effective learning: a BEME systematic review. Medical teacher 2005; 27:10–28. Disclosures None.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,033
score de la tête « metaresearch » (Gemma)0,121
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,095
Score d'incertitude au seuil0,316

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0330,121
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0060,008
Études des sciences et des technologies0,0010,002
Communication savante0,0060,004
Science ouverte0,0020,003
Intégrité de la recherche0,0030,002
Charge utile insuffisante (le modèle a refusé de juger)0,0950,016

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 Opus0,086
Tête enseignante GPT0,447
Écart entre enseignants0,361 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
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

Citations0
Publié2013
Routes d'admission1
Résumé présentoui

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Même revueSimulation in Healthcare The Journal of the Society for Simulation in HealthcareMême sujetSimulation-Based Education in HealthcareTravaux en français237 207