‘There shouldn't be anything wrong with not knowing’: epistemologies in simulation
Bibliographic record
Abstract
CONTEXT: Medical education embraces simulation-based education (SBE). However, key SBE features purported to support learning, such as learner safety and learning through experience and error, may not align with the dominant culture of medicine, in which portraying confidence and certainty about one's knowledge prevails. Misaligned conceptions about knowledge and learning may produce unintended negative effects, including the suboptimal implementation of SBE, which could consequently compromise SBE and its outcomes. METHODS: To uncover the epistemological beliefs of students experiencing SBE, we conducted a theory-informed analysis of interviews with 24 pre-clerkship medical students following their participation in an SBE training study. Our analysis borrowed from coding methods common in constructivist grounded theory and used Hofer and Pintrich's four dimensions of epistemology as sensitising concepts. RESULTS: Participants subscribed to a dominant view of knowledge as consisting of concrete facts, derived from external sources. By contrast, they described but did not prioritise a conception of building their own knowledge through different learning experiences. Participants positioned experts (i.e. teaching faculty members) as the ultimate knowledge validators through their presence and feedback. Participants also noted that faculty staff could counter medicine's pressures to perform with certainty and confidence at all times by instead embodying and modelling an authentic appreciation of learning through experiences, errors and discovery. CONCLUSIONS: Medicine's tendency to idealise the objective pursuit of singular truths may compromise the purported culture of SBE as a space for learning many wide-ranging aspects of medicine, including how and when to innovate and deviate from norms. Explicit attempts to bridge the epistemological beliefs of medicine and SBE may better enable the realisation of safe experiential learning. Faculty members are positioned to play key roles in enabling this bridging.
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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 | 0.037 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.109 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".