Examining the clinical learning environment through the architectural avenue
Bibliographic record
Abstract
Medical education has traditionally focused on the learners, the educators, and the curriculum, while tending to overlook the role of the designed environment. Experience indicates, however, that processes and outcomes of medical education are sensitive to the qualities and disposition of the spaces in which it occurs. This includes the clinical education within the patient care environment, termed the clinical learning environment (CLE). Recognition of this has informed the design of some new clinical learning spaces for the past decade. Competency-based clinical education can drive design requirements that differ materially from those associated with general purpose educational or clinical spaces. In this article, we outline two conceptual frameworks: (i) materialist spatiality and (ii) actor-network theory and consider how they can guide the design of spaces to support competency-based medical education and to guide the evaluation and discussion of the educational impacts of the spaces once built. We illustrate the use of these frameworks through discussion of the educational ambitions that underpinned the design of some recent clinical educational spaces. We close with practical points for consideration by educators and designers.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".