Structured Clinical Insights Modules (SCIMs): High fidelity simulated scenarios for learning clinical skills and clinical reasoning
Bibliographic record
Abstract
Introduction 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. Background Feedback from senior medical students and their supervising clinicians identified the need to supplement the skills learning from clinical placements with structured learning opportunities. Methods A learning methodology was developed that combines the reasoning-development approach of Problem Based Learning with high fidelity clinical simulation. Students were divided into teams of five (designated as a 'registrar' and four 'interns') who managed a simulated patient through an evolving story over the period of a week. Innovative elements included extensive use of trained simulated patients and relatives, technological simulations, after hours 'on call' experiences and a simulated court case involving cross examination of learners by an experienced barrister. 150 Year 3 medical students undertook the program in 2009. Evaluation was undertaken using standardised questionnaires, as well as Interpretative Phenomenological Analysis of learners' reflective journals to identify evidence of learning in the affective domain (values, attitudes and human engagement). Results The program was very positively received by learners (mean subjective effectiveness score of 6.38 on a 7-point Likert scale). Analysis of journals revealed numerous examples of deep reflection and affective learning in association with the program. Conclusion The SCIMs methodology shows promise for promoting the initial acquisition of essential clinical skills in a safe environment, as an adjunct to clinical attachments. References 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. Spalding WB. The undergraduate medical curriculum (1969 model): McMaster University. Canadian Medical Association Journal 100(14):659-664, 1969. Flanagan B, Nestel D, Joseph M. Making patient safety the focus: Crisis Resource Management in the undergraduate curriculum. Medical Education 38:56-66, 2004. Smith JA. Beyond the divide between cognition and discourse: Using Interpretative Phenomenological Analysis in health psychology. Psychology and Health 11:261-71, 1996. Rogers GD, McConnell H, Milligan E, Lombard M, Jones de Rooy N. Desperately seeking evidence of learning in the affective domain: Interpretative Phenomenological Analysis of clinical learners' reflective journals. Paper at this conference, 2010.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".