The Use of Simulation Education in Competency Assessment: More Questions than Answers
Notice bibliographique
Résumé
We would like to thank Dr. Edler for her interest in our article.1First and foremost, in no way are we suggesting that simulation education be discarded, but, as Dr. Edler comments, there is a pressing need to ensure that we are assessing its impact using valid, reliable tools.We also agree with Dr. Edler that generalizability is the way of the future and in fact is the analytic basis of our current ongoing study, designed to examine the psychometric properties of a newly developed obstetric team performance tool.Our study was based on a sample size and using a design that would make the use of multifaceted generalizability analyses complicated at best. In fact, a generalizability analysis would likely be unstable because individuals were partially nested within team, and team was nested within scenario, and the number of actual performances was small for a generalizability analysis, which is why, ultimately, we adopted the analytic techniques we did for the study in question.However, rater was fully crossed with all the dimensions that Dr. Elder described as other potential sources of variance, and it was the interrater reliability of the Human Factors Rating Scale that was a problem. Although we agree that other facets might add further error, there is no reason to believe that the interrater reliability would be better understood through the partitioning of other variances. With regard to the Human Factors Rating Scale, as Dr. Edler suggests, undoubtedly “something else is going on here.” However, based on our findings in the study, we have become substantially less interested in figuring out that “something else” than we might otherwise have been.Instead, we decided that it will ultimately be more profitable to start from scratch, developing a tool that is generated out of the practice and experience of healthcare teams, rather than to spend our resources investigating a better understanding of what we think will ultimately prove to be the wrong tool.Although we have independently come to the same opinions as Dr. Edler regarding the types of studies needed, based on our preliminary data, we decided that we will be looking to tools other than the Human Factors Rating Scale as the subject of these more elaborate generalizability analyses.*Women’s College Hospital, University of Toronto, Toronto, Ontario, Canada. pam.morgan@utoronto.ca
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,046 | 0,216 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,006 |
| Communication savante | 0,004 | 0,018 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,013 | 0,019 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».