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Enregistrement W2321113260 · doi:10.1097/sih.0000000000000075

Debriefing and Feedback

2015· editorial· en· W2321113260 sur OpenAlexaff
Stéphane Voyer, Rose Hatala

Notice bibliographique

RevueSimulation in Healthcare The Journal of the Society for Simulation in Healthcare · 2015
Typeeditorial
Langueen
DomaineMedicine
ThématiqueSimulation-Based Education in Healthcare
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésDebriefingContext (archaeology)PsychologySet (abstract data type)WonderAction (physics)Medical educationSocial psychologyComputer scienceMedicine

Résumé

récupéré en direct d'OpenAlex

Debriefing is a key part of simulation. Whether it occurs at the end of a simulation or is woven into the simulation itself, debriefing is the time and space where learners “reflect on action, identify performance gaps, discuss areas for improvement, and consolidate knowledge and skills so that the latter can be applied in real practice to improve health care and patient outcomes.”1 As a critical element of simulation education, it is interesting that debriefing poses such a challenge for even the most seasoned educators—“Even after 23 years, the most experienced of us are still learning to debrief.”2 Recently, there has been an inclination to pursue research questions that address the “technical” aspects of debriefing, in an effort to determine what combinations of duration, timing, use of video, and so on lead to the greatest educational benefits. An excellent systematic review by Cheng et al1 highlights that although these debriefing features may influence learning outcomes, the effect sizes are notably small and inconsistent. We are left to wonder whether there are other “technical” issues that need to be looked at further or whether the keys to better debriefing might instead come from thinking about debriefing in an entirely different way. An example of rethinking the debriefing problem is addressed by Rudolph et al.3 They describe how to establish a psychologically safe context for learning using a set of educational practices to create the conditions where learners “feel safe enough to embrace being uncomfortable” in the simulated environment.3 In fostering the “safe container,” the instructor (among other things) conveys a commitment to respecting the learner and understanding the learner’s perspective. The establishment of this safe container for learning is done before the simulation session, in a prebriefing. The ideas put forward by Rudolph et al speak to a much deeper reality in simulation education—no matter how technically sound a debriefing is, there are social and interpersonal matters that need to be addressed if the debriefing is to have its desired impact. This is true in simulation and is equally true in the broader context of medical education. We come at this issue as medical educators and see many parallels between simulation-based debriefing and feedback in clinical education. Similar to the simulation community’s struggles with debriefing, the medical education research community has been working on the “feedback problem” for a long time. Most educators agree that feedback is the information provided to a learner on the gap between their performance and a standard, with suggestions on how the gap might be bridged.4 In parallel with debriefing, feedback is recognized as an important component of effective education. How to give and receive meaningful feedback, however, remains a hot research topic. Similar to the many models of debriefing that are outlined in the Concepts and Commentary article by Cheng et al1 in this issue of Simulation in Healthcare, many feedback models have been proposed, but in practice, each one falls short—feedback remains an often challenging, uncomfortable part of clinical education. Recently, several medical education research groups have begun to pay closer attention to the broader sociocultural and interpersonal elements that shape any feedback exchange. As an example, Watling et al5 have proposed that for feedback to have a meaningful impact on performance, it must originate from a credible source. Credibility, in this context, is determined in many ways and includes judgments such as “Do I respect this person?” and “How has this person come to make this assessment of me?” In this light, recipients of feedback are seen not as passive vessels, taking in all feedback equally, but instead as filters of multiple sources of feedback, picking and choosing among the varied sources of available information. Qualitative work by Mann et al6 has described a number of tensions that influence an individual’s receptivity to feedback during assessment of clinical performance. These include tensions within the individual (wanting but fearing feedback), tensions between individuals providing and receiving feedback (where credibility may have a role), and tensions in the learning environment (how behavior changes when being observed). Within the individual, there are complex interactions among fear, confidence, and clinical reasoning that may influence the receptivity to feedback.7 Although we have only highlighted a few of the medical education studies in this regard, it is evident that important insights are being gained, which influence how we think about feedback. What does this mean for debriefing and simulation? The good news is that simulation educators are already in a great position to provide meaningful feedback to learners by virtue of a number of features that should positively influence the exchange of meaningful feedback. Simulation sessions typically begin with a prebriefing, and as outlined by Rudolph et al,3 this can be a very effective approach for establishing a safe learning environment; in clinical education, the clinical context is not always as clearly “safe” for education. Simulation also provides the opportunity for direct observation of the performance of a clinically relevant task, enhancing credibility in a way that is often lacking in clinical settings where learners are seldom directly observed. Finally, simulation settings provide supportive learning environments where learners can make mistakes without causing real harm. The better news is that there is a clear way forward for research in debriefing. Simulation education researchers might borrow from the broader medical education literature the set of new insights into the sociocultural and interpersonal contexts that shape feedback (and debriefing) interactions. Complementing many of the faculty development issues in debriefing identified by Cheng et al,1 these recent insights into the sociocultural aspects of feedback might form the basis of entirely new faculty development strategies, ones that focus not only on the technical aspects of the debriefing but also on creating the conditions necessary for meaningful debriefing to take place. Research suggests that these contexts and conditions are highly influential, and there is every reason to believe that they may also have a role to play in shaping debriefing. Effective debriefing in simulation has many parallels to meaningful feedback in clinical education, and the 2 worlds have a lot to offer each other.

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)0,089
score de la tête « metaresearch » (Gemma)0,345
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,089
Score d'incertitude au seuil0,468

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0890,345
Méta-épidémiologie (sens strict)0,0030,001
Méta-épidémiologie (sens large)0,0030,003
Bibliométrie0,0040,002
Études des sciences et des technologies0,0030,003
Communication savante0,0050,004
Science ouverte0,0040,005
Intégrité de la recherche0,0040,008
Charge utile insuffisante (le modèle a refusé de juger)0,0830,028

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 Opus0,052
Tête enseignante GPT0,412
Écart entre enseignants0,359 · 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'étudeSans objet
Domainenon disponible
GenreÉditorial

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

En bref

Citations38
Publié2015
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