Simulation‐based education for novices: complex learning tasks promote reflective practice
Bibliographic record
Abstract
CONTEXT: Simulated clinical immersion (SCI), in which clinical situations are simulated in a realistic environment, safely and gradually exposes novices to complex problems. Given their limited experience, undergraduate students can potentially be quite overwhelmed by SCI learning tasks, which may result in misleading learning outcomes. Although task complexity should be adapted to the learner's level of expertise, many factors, both intrinsic and extraneous to the learning task, can influence perceived task complexity and its impact on cognitive processes. OBJECTIVES: The purpose of this mixed-methods study was to understand the effects of task complexity on undergraduate pharmacy students' cognitive load, task performance and perception of learning in SCI. METHODS: A total of 167 second-year pharmacy students were randomly assigned to undertake one simple and one complex learning task in SCI consecutively. Participants' cognitive load was measured after each task and debriefing. Task performance and time on task were also assessed. As part of a sequential explanatory design, semi-structured interviews were conducted with students showing maximal variations in intrinsic cognitive load to elucidate their perceptions of learning when dealing with complexity. RESULTS: Although the complex task generated significantly higher cognitive load and time on task than the simple task, performance was high for both tasks. Qualitative results revealed that a lack of clinical experience, an unfamiliar resource in the environment and the constraints inherent to SCI, such as time limitations, hindered the clinical reasoning process and led to poorer self-evaluation of performance. Simple tasks helped students gain more self-confidence, whereas complex tasks further encouraged reflective practice during debriefings. CONCLUSIONS: Although complex tasks in SCI were more cognitively demanding and took longer to execute, students indicated that they learned more from them than they did from simple tasks. Complex tasks constitute an additional challenge in terms of clinical reasoning and thus provide a more valuable learning experience from the student's perspective.
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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 | 0.002 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".