CLEIMS: first report on a randomised trial of extended clinical immersion simulation to contextualise medical student learning and develop clinical reasoning
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".