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Enregistrement W1760893596 · doi:10.1007/s11999-015-4512-6

Simulation for Teaching Orthopaedic Residents in a Competency-based Curriculum: Do the Benefits Justify the Increased Costs?

2015· article· en· W1760893596 sur OpenAlexafffundabout
Markku Nousiainen, Sydney McQueen, Peter C. Ferguson, Benjamin A. Alman, William J. Kraemer, Oleg Safir, Richard K. Reznick, Ranil Sonnadara

Notice bibliographique

RevueClinical Orthopaedics and Related Research · 2015
Typearticle
Langueen
DomaineMedicine
ThématiqueSurgical Simulation and Training
Établissements canadiensQueen's UniversityHealth Sciences CentreMcMaster UniversityUniversity of TorontoSunnybrook Health Science Centre
Organismes subventionnairesUniversity of TorontoOntario Ministry of Health and Long-Term Care
Mots-clésCurriculumMedicineMedical educationGraduate medical educationMedical physicsAccreditationPsychologyPedagogy

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Although simulation-based training is becoming widespread in surgical education and research supports its use, one major limitation is cost. Until now, little has been published on the costs of simulation in residency training. At the University of Toronto, a novel competency-based curriculum in orthopaedic surgery has been implemented for training selected residents, which makes extensive use of simulation. Despite the benefits of this intensive approach to simulation, there is a need to consider its financial implications and demands on faculty time. QUESTIONS/PURPOSES: This study presents a cost and faculty work-hours analysis of implementing simulation as a teaching and evaluation tool in the University of Toronto's novel competency-based curriculum program compared with the historic costs of using simulation in the residency training program. METHODS: All invoices for simulation training were reviewed to determine the financial costs before and after implementation of the competency-based curriculum. Invoice items included costs for cadavers, artificial models, skills laboratory labor, associated materials, and standardized patients. Costs related to the surgical skills laboratory rental fees and orthopaedic implants were waived as a result of special arrangements with the skills laboratory and implant vendors. Although faculty time was not reimbursed, faculty hours dedicated to simulation were also evaluated. The academic year of 2008 to 2009 was chosen to represent an academic year that preceded the introduction of the competency-based curriculum. During this year, 12 residents used simulation for teaching. The academic year of 2010 to 2011 was chosen to represent an academic year when the competency-based curriculum training program was functioning parallel but separate from the regular stream of training. In this year, six residents used simulation for teaching and assessment. The academic year of 2012 to 2013 was chosen to represent an academic year when simulation was used equally among the competency-based curriculum and regular stream residents for teaching (60 residents) and among 14 competency-based curriculum residents and 21 regular stream residents for assessment. RESULTS: The total costs of using simulation to teach and assess all residents in the competency-based curriculum and regular stream programs (academic year 2012-2013) (CDN 155,750, USD 158,050) were approximately 15 times higher than the cost of using simulation to teach residents before the implementation of the competency-based curriculum (academic year 2008-2009) (CDN 10,090, USD 11,140). The number of hours spent teaching and assessing trainees increased from 96 to 317 hours during this period, representing a threefold increase. CONCLUSIONS: Although the financial costs and time demands on faculty in running the simulation program in the new competency-based curriculum at the University of Toronto have been substantial, augmented learner and trainer satisfaction has been accompanied by direct evidence of improved and more efficient learning outcomes. CLINICAL RELEVANCE: The higher costs and demands on faculty time associated with implementing simulation for teaching and assessment must be considered when it is used to enhance surgical training.

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)0,005
score de la tête « metaresearch » (Gemma)0,045
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,011
Score d'incertitude au seuil0,050

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,045
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0030,002
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0110,001

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 Opus0,140
Tête enseignante GPT0,462
Écart entre enseignants0,322 · 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'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

Citations88
Publié2015
Routes d'admission3
Résumé présentoui

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