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Enregistrement W2331536196 · doi:10.1097/01.sih.0000441595.05212.f5

Board 343 - Research Abstract Development and Evaluation of a Contextually Relevant Measure of Cognitive Load for Simulation-Based Psychomotor Skills Training (Submission #951)

2013· article· en· W2331536196 sur OpenAlexaffabout
Faizal Haji, Robert L. Martin, Gary Ng, James M. Drake, Adam Dubrowski

Notice bibliographique

RevueSimulation in Healthcare The Journal of the Society for Simulation in Healthcare · 2013
Typearticle
Langueen
DomaineMedicine
ThématiqueCardiac, Anesthesia and Surgical Outcomes
Établissements canadiensSickKids Foundation
Organismes subventionnairesnon disponible
Mots-clésPsychomotor learningCognitive loadCognitionComputer scienceTask (project management)Cognitive psychologyPsychologyApplied psychologyEngineering

Résumé

récupéré en direct d'OpenAlex

Introduction/Background Theoretically-based research exploring instructional design in healthcare simulation has emerged as a top priority.1–3 In turn, interest in cognitive load theory as a foundation for empirical investigation of instructional design principles in simulation has grown.1,4 An essential precursor to this line of inquiry is the development and evaluation of cognitive load (CL) measures that are appropriate for the healthcare simulation setting. To be effective, these measures should be unintrusive, sensitive to cognitive demands imposed by the simulated task and natural to the performer.5 The objectives of this study were to: 1) develop contextually relevant measures of CL based on secondary-task methodology and 2) generate preliminary validity evidence6 supporting their use in simulation-based psychomotor skills training. It was hypothesized that: 1) these measures of secondary-task performance would be sensitive to variations in CL within novices as cognitive demands change and between novices and experts when performing a psychomotor primary-task, and 2) similar patterns would be observed between experts and novices on subjective measures of cognitive load and primary-task performance. Methods . We developed a virtual vital signs monitor with a built-in visual stimulus detection secondary-task, in which participants monitor a baseline heart-rate and press a foot-pedal each time a pre-determined change (bradycardia or tachycardia) is observedThe software subsequently records two performance metrics: stimulus-detection error rate (SDER) and recognition reaction time (RRT)To evaluate the sensitivity of these metrics to variations in CL during simulation-based psychomotor skills training, five experts (surgical residents) and seven novices (medical students) completed a baseline stimulus-detection trial and a dual-task trial consisting of one-handed surgical knot tying on a part-task trainer, while monitoring for changes in heart-rateFollowing the dual-task trial, participants also completed a subjective rating of mental effort (SRME) using a previously developed scale.7 Primary-task (knot-tying) performance was assessed by total movements (TM) and time to complete (TC) a square knot.8 The first hypothesis was tested by analyzing differences in RRT and SDER from baseline to dual-task between experts and novices, using 2x2 repeated measures ANOVA and the Tukey test for post-hoc comparisonsThe second hypothesis was tested by analyzing differences between experts and novices SRME and on knot-tying performance, using the Kruskal-Wallis test and independent sample t-test respectively. Results . Analysis of secondary-task performance demonstrated a significant interaction between expertise (novice vsexpert) and task (single vsdual-task) for RRT (F(1,10)=9.947, p<0.01, partial eta2=0.89) and SDER (F(1,10)=81.133, p<0.0001, partial eta2=0.89)Pairwise comparisons revealed a significant increase in RRT and SDER from baseline to dual-task among novices (q=6.18, p<0.025 and q=16.45, p<0.01 respectively) but not among expertsIn addition, experts had significantly lower RRT and SDER compared to novices during dual-tasking (q=5.21, p<0.05 and q=14.88, p<0.01 respectively) but not at baselineSimilarly, compared to novices, experts had significantly lower dual-task SRME (chi2=5.316, p<0.021) and superior primary task performance with respect to TC (t=4.939, p<0.004), and TM (t=4.748, p<0.005). Conclusion We have developed an instrument for assessing CL that employs a contextually relevant secondary task (response to changes in vital signs). The measures generated from this instrument are sensitive to variations in CL among novices as cognitive demands change (i.e. single to dual-tasking) and between novices and experts performing a psychomotor skill. The difference in performance between novices and experts on these measures are similar to those seen on primary task performance (TC and TM) and subjective ratings of cognitive load, demonstrating preliminary validity evidence in the category of “response to other variables”6 for the two CL measures generated by our instrument (RRT and SDER). The Results indicate this instrument may be effective for measuring cognitive load during simulation-based psychomotor skills training of novice learners. References 1. Issenberg SB, Ringsted C, Østergaard D, Dieckmann P: Setting a Research Agenda for Simulation-Based Healthcare Education: Simulation in Healthcare 2011; 6(3):155–167. 2. Dieckmann P, Phero JC, Issenberg SB, Kardong-Edgren S, Østergaard D, Ringsted C: The first Research Consensus Summit of the Society for Simulation in Healthcare: conduction and a synthesis of the Results. Simulation in Healthcare 2011; 6(Suppl):S1–S9. 3. Cook DA, Hamstra SJ, Brydges R, Zendejas B, Szostek JH, Wang AT, Erwin PJ, Hatala R: Comparative effectiveness of instructional design features in simulation-based education: Systematic review and meta-analysis. Medical Teacher 2013; 35(1):e844–75. 4. van Merrienboer JJG, Sweller J: Cognitive load theory in health professional education: design principles and strategies. Medical Education 2010; 44(1):85–93. 5. Carswell C, Clarke D, Seales W: Assessing Mental Workload During Laparoscopic Surgery. Surgical Innovation 2005; 12(1):80–90. 6. Downing S: Validity - on the meaningful interpretation of assessment data. Medical Education 2003; 37:830–837. 7. Paas FG, Van Merriënboer JJG, Adam JJ: Measurement of cognitive load in instructional research. Perceptual and Motor Skills 1994; 79:419–430. 8. Xeroulis G, Park J, Moulton C, Reznick R, LeBlanc V, Dubrowski A: Teaching suturing and knot-tying skills to medical students: A randomized controlled study comparing computer-based video instruction and (concurrent and summary) expert feedback. Surgery 2007; 141(4):442–449. Disclosures Royal College of Physicians and Surgeons of Canada Fellowship for Studies in Medical Education L3 Communications, Montreal Quebec.

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)0,015
score de la tête « metaresearch » (Gemma)0,009
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
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 enseignants0,303
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0150,009
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
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 Opus0,169
Tête enseignante GPT0,460
Écart entre enseignants0,291 · 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

Citations0
Publié2013
Routes d'admission2
Résumé présentoui

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