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Enregistrement W2317763945 · doi:10.1097/01.sih.0000441635.22315.ed

Board 383 - Research Abstract Examining the Effects of a Student-Led Learning Paradigm in a Simulation-Based Surgical Skills Course (Submission #564)

2013· article· en· W2317763945 sur OpenAlexaffabout
Sydney McQueen, Polina Mironova, Oleg Safir, Markku Nousiainen, William J. Kraemer, Peter C. Ferguson, Benjamin A. Alman, Richard K. Reznick, Ranil Sonnadara

Notice bibliographique

RevueSimulation in Healthcare The Journal of the Society for Simulation in Healthcare · 2013
Typearticle
Langueen
DomaineMedicine
ThématiqueInnovations in Medical Education
Établissements canadiensQueen's University
Organismes subventionnairesnon disponible
Mots-clésSession (web analytics)Medical educationParadigm shiftPsychologyDreyfus model of skill acquisitionMedicineComputer science

Résumé

récupéré en direct d'OpenAlex

Introduction/Background Recent changes in healthcare training, such as increasing demands on faculty time, reduced opportunities for teaching in the clinical setting and increasing awareness for patient safety, have led medical educators to rely more on simulation-based programs to supplement traditional clinical teaching.1–4 One such program is the Toronto Orthopaedic Boot Camp (TOBC), an intensive course designed to teach core surgical skills to incoming orthopaedic residents at the University of Toronto.5 Data from this program have revealed that it is possible to persistently advance the technical skills of first year residents to the level of senior residents for targeted tasks after just one month.6,7 However, technical skill development is only one aspect of surgical training and there is a recognized need to emphasize non-technical skills and promote professional identity formation.8,9 In the present study, we investigate the impact of implementing a student-led learning (SLL) paradigm on the early acquisition of non-technical skills. Methods Twelve incoming orthopaedic residents participated in this research, which was embedded in the month long TOBC skills course. Residents were randomly divided into two groups: six were taught using a new paradigm focused on supervised, student-led exploration and practice (SL group), while the other six were taught using a traditional, instructor-led paradigm (IL group).6,7 A typical day consisted of a didactic teaching session followed by an extended practical session in a simulation laboratory, with the instructional methodology governed by the assigned paradigm. Trained observers systematically documented the interactions that took place over the course of the month. Upon completion of the program, residents completed self-efficacy and exit questionnaires. Confidential interviews were conducted with the residents and primary instructors and senior faculty were polled for informal observations. Field observation notes were analyzed thematically. Interview responses were transcribed, checked for accuracy and analyzed for common themes by two independent raters. We then used a qualitative outcomes analysis approach to interpret our findings. Results Residents from both SL and IL groups were able to perform the targeted technical skills to an acceptable standard by the end of the TOBC program. We observed differences in the non-technical skills acquired by the two groups. SL participants exhibited more peer-to-peer interactions and both asked and were asked more interpretative questions. SL residents also reported feeling a greater sense of control over their learning than their IL counterparts. Interviews revealed that the IL group regarded their instructors as resources who could teach them the task at hand, whereas the SL group described their instructors as facilitators, offering guidance when asked but allowing them to explore skills and techniques for themselves. The instructors reported more cooperation and collegiality amongst the SL group. Conclusion Student-led learning was found to effectively promote and enhance interactions both between peers and also between trainees and their instructors. The SL paradigm also afforded residents pedagogic space to direct their own learning, which enabled them to feel more in control of the learning process. All of the traits promoted by the SL paradigm are known to contribute to effective learning,10–15 as well as the development of professional identity.16–19 The flexible and socially rich nature of the SL paradigm also appears to promote effective decision making, teamwork and leadership skills.9,20,21 Anecdotal reports from senior faculty suggest that this approach may lead to a better understanding of several core CanMEDS roles early in residency. Our data shows that well constructed simulation-based programs can privilege both technical and non-technical skills early in residency.7 Preliminary evidence suggests that such programs can better prepare new trainees for independent clinical practice, though further study is needed. References 1. Carter BN: The fruition of Halsted’s concept of surgical training. Surgery 1952; 32(3): 518–527. 2. Reznick RK, and MacRae H: Medical education - Teaching surgical skills - Changes in the wind. N Engl J Med 2006; 355(25): 2664–2669. 3. Ericsson KA, Krampe RT, and Tesch-romer C: The role of deliberate practice in the acquisition of expert performance. Psychol Rev 1993; 100(3): 363–406. 4. Ahlberg G, Enochsson L, Gallagher AG, et al: Proficiency-based virtual reality training significantly reduces the error rate for residents during their first 10 laparoscopic cholecystectomies. Am J Surg 2007; 193(6): 797–804. 5. Sonnadara RR, Van Vliet A, Safir O, et al: Orthopedic boot camp: examining the effectiveness of an intensive surgical skills course. Surgery 2011; 149(6): 745–749. 6. Sonnadara RR, Garbedian S, Safir O, et al: Orthopaedic Boot Camp II: examining the retention rates of an intensive surgical skills course. Surgery 2012; 151(6): 803–807. 7. Sonnadara R, Garbedian S, Safir O, et al: Orthopaedic Boot Camp III: Examining the efficacy of student-regulated learning during an intensive laboratory-based surgical skills course. Surgery 2013; 154(1): 29-33. 8. Jarvis-Selinger S, Pratt DD, and Regehr G: Competency is not enough: integrating identity formation into the medical education discourse. Acad Med 2012; 87(9): 1185–1190. 9. Sharma B, Mishra A, Aggarwal R, and Grantcharov TP: Non-technical skills assessment in surgery. Surg Oncol 2010; 20(3): 169–177. 10. Knowles MS: Self-Directed Learning: A Guide for Learners and Teachers. New York, Association Press, 1975, pp 1-140. 11. Zimmerman BJ: A social cognitive view of self-regulated academic learning. J Educ Psychol 1989; 81(3): 329–339. 12. Bruner JS: Acts of Meaning. Cambridge, Harvard University Press, 1990, pp 71-102. 13. Zimmerman BJ: Self-Regulated Learning and Academic Achievement: An Overview. Educ Psychol 1990; 25: 3–17. 14. Brydges R, Carnahan H, Safir O, and Dubrowski A: How effective is self†guided learning of clinical technical skills? It’s all about process. Med Educ 2009; 43(6): 507–515. 15. Keetch KM, and Lee TD: The Effect of Self-Regulated and Experimenter-Imposed Practice Schedules on Motor Learning for Tasks of Varying Difficulty. Res Q Exercise Sport 2007; 78(5): 476–486. 16. Sandars J, Homer M, Pell G, and Croker T: Web 2.0 and social software: the medical student way of e-learning. Med Teach 2008; 30(3): 308–312. 17. Irby DM, Cooke M, and OʼBrien BC: Calls for Reform of Medical Education by the Carnegie Foundation for the Advancement of Teaching: 1910 and 2010. Acad Med 2010; 85(2): 220–227. 18. Varga-Atkins T, Dangerfield P, and Brigden D. Developing professionalism through the use of wikis: A study with first-year undergraduate medical students. Med Teach 2010; 32(10): 824–829. 19. Cruess RL, and Cruess SR: Teaching professionalism: general principles. Med teach 2006; 28(3): 205–208. 20. Topping K: Peer Assessment Between Students in Colleges and Universities. Rev Educ Res 1998; 68(3): 249–276. 21. Topping KJ: Trends in Peer Learning. J Educ Psychol 2005; 25(6): 631–645. Disclosures Smith and Nephew, Inc. Zimmer, Inc.

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)0,011
score de la tête « metaresearch » (Gemma)0,008
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesIntégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,169
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0110,008
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,003
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,060
Tête enseignante GPT0,468
Écart entre enseignants0,408 · 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'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

Citations1
Publié2013
Routes d'admission2
Résumé présentoui

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Même revueSimulation in Healthcare The Journal of the Society for Simulation in HealthcareMême sujetInnovations in Medical EducationTravaux en français237 207