CLEIMS: first report on a randomised trial of extended clinical immersion simulation to contextualise medical student learning and develop clinical reasoning
Notice bibliographique
Résumé
Aims To determine the educational effectiveness of extended clinical immersion simulation to develop medical students' clinical reasoning and contextualize learning. Background Scarcity of clinical placements and heightened concern for patient safety have led to an increased focus on simulation methodologies for the early acquisition of clinical technical, human engagement and reasoning skills, in parallel with clinically-based learning opportunities in undergraduate medical education.(1) Students and clinical supervisors recognise that some clinical and reasoning skills are difficult to acquire solely through experiential learning in clinical settings, especially in shorter medical courses with shorter clinical placements. The Clinical Learning through Extended Immersion in Medical Simulation (CLEIMS) methodology combines the reasoning-development approach of Problem Based Learning(2) with high fidelity clinical simulation.(3) Students are divided into medical teams, each comprising 2-4 'interns' and a designated 'registrar', who manage a simulated patient through an evolving story over the period of a week. Innovative elements include extensive use of trained simulated patients and relatives, technological simulations for emergency management and simulated after hours 'on call' experiences. A pilot of the methodology was extremely positively received by learners but it is resource intensive and definitive evidence of educational effectiveness will be required for sustainability. Methods Local ethics committee approval was obtained. 2010 Year 3 MBBS students were invited to enrol in the study and 65% of the cohort did (n = 98). Participants were randomised 1:1 to receive either the full CLEIMS methodology (intervention arm) or just the associated seminars and workshops without the contextualising extended simulation (control arm), during their one 'in-school week' in each of 2010 (Year 3) and 2011 (Year 4). The two arms will be compared in relation to knowledge and script concordance (reasoning) written tests, as well as a practical clinical skill test, at the end of each week, as the primary endpoint. Secondary endpoints will include performance in summative OSCEs and evidence of affective-domain learning on Interpretative Phenomenological Analysis of reflective journals.(4) Results 98 students have enrolled in the study, which will proceed during 2010. By the time of the conference it is anticipated that 82 participants (84%) will have completed the first study week and primary endpoint data will be available for interim analysis. Conclusions First data from this randomised educational trial will be presented. References 1. Okuda Y, Bryson EO, DeMaria S, et al. The utility of simulation in medical education: What is the evidence? Mt Sinai Journal of Medicine 76(4):330-43, 2009. 2. Spalding WB. The undergraduate medical curriculum (1969 model): McMaster University. Canadian Medical Association Journal 100(14):659-664, 1969. 3. Flanagan B, Nestel D, Joseph M. Making patient safety the focus: Crisis Resource Management in the undergraduate curriculum. Medical Education 38:56-66, 2004. 4. Smith JA. Beyond the divide between cognition and discourse: Using Interpretative Phenomenological Analysis in health psychology. Psychology and Health 11:261-71, 1996.
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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 | 0,009 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,005 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,020 | 0,003 |
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 ».