Architecture’s creative contributions to healing: oncology patients in the Cedars Cancer Centre
Bibliographic record
Abstract
BACKGROUND: The present state of Evidence-Based Design (EBD), the dominant paradigm in healthcare architecture, arguably limits architecture’s creative contributions to healing. EBD is founded upon the principle of designing built environments based on research to optimize outcomes. However, EBD’s current heavy focus on measurable results may not sufficiently address the multidimensionality of patienthood. Using EBD, designers of Montreal’s new Cedars Cancer Centre endeavoured to create a patient-centered space that holistically addresses oncology patients’ needs. PURPOSE: Using ethnographic and architectural approaches, this study evaluates whether oncology patients’ lived experiences of the Cancer Centre correspond to designers’ original qualitative intentions for a supportive, healing environment. METHODS AND RESULTS: This paper presents results from thematic analysis of annotated architectural floor plans and transcribed interviews with sixteen Cancer Centre outpatients between January and May 2017. Themes are content-analyzed and organized according to original design aims: 1) improving patient-staff relations, 2) reducing patient stress and anxiety and 3) empowering patients. SIGNIFICANCE: Our oncology design study is the first of its kind to delve into patient backstories, intertwining them with in-depth analysis of the design process and final architectural product.Our findings promote an analytical discussion about EBD and highlight new insights about oncology design and cancer patients’ spatial needs. More broadly, we propose implications for the relationship between architecture and Whole Person Care (WPC), namely: 1) drawing out architectural lessons that increase empathy towards space’s impact on illness, thus deepening health professionals’ relationships with patients, and 2) conceptualizing application of WPC principles in healthcare design.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.018 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".