‘I feel like I sleep here’: how space and place influence medical student experiences
Bibliographic record
Abstract
CONTEXT: Buildings and learning spaces contribute in crucial ways to people's experiences of these spaces. However, this aspect of context has been under-researched in medical education. We addressed this gap in knowledge by using the conceptual notions of space and place as heuristic lenses through which to explore the impact of a new medical school building on student experiences. METHODS: We carried out an exploratory case study to explore the impact of a new medical school building on student experiences. Data were collected from archived documents (n = 50), interviews with key stakeholders (n = 17) and focus group discussions with students (n = 17 participants) to provide context and aid triangulation. Data coding and analysis were initially inductive and conducted using thematic analysis. After themes had emerged, we applied the concepts of boundary objects, liminal space and Foucault's panopticon to provide a framework for the data. RESULTS: There were specific visions and intentions for the place (the location) and space (the facilities) of the new medical school building (e.g. it was positioned to facilitate flow between educational and clinical settings). However, the unintentional consequences of the planning were that students felt disconnected from the wider university, trapped on the health care campus, and under pressure to behave not like students but in a manner they believed to be expected by clinical staff and patients. CONCLUSIONS: Despite much effort and a focus on creating an idyllic space and place, the new medical school had both positive and (unintentionally) negative impacts on student experiences. These findings highlight the importance of reflecting on, and exploring, how space and place may influence and shape students' learning experiences during the formative years of their development of a professional identity, a necessary consideration when planning new medical school learning spaces or changing these spaces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".