Spatial experience of cancer inpatients in the oncology wards: A qualitative study in visual design aspects
Bibliographic record
Abstract
PURPOSE: This paper aims to explore how the visual characteristics of cancer wards' interior spaces can be improved based on the combined visual design themes to help cancer inpatients' spatial experience and relieved state of mind. Accordingly, we present a visual design framework that can be applied in oncology wards. METHOD: This study adopts the Ulrich-supportive design theory as a theoretical framework using two main methodological phases: observation of cancer wards and interviews with professional caregivers. The first phase critically explores hospital cancer wards' interactive aesthetical and visual interior characteristics. Next, we adjusted the visual criteria based on the Post Occupation Evaluation (POE) method to develop the interview questions. Interviews were conducted with experienced nurses, oncologists, and a general physician, all from a cancer ward at McGill University Health Center (MUHC) in Montreal, Canada. RESULTS: We presented 11 main themes in the categories of color and light, natural/artistic images, way-finding, and visual clutter. To present and justify our visual design framework, these main themes were then classified based on the common goals, resulting in four combined themes: applying simplicity and usability; developing naturality; creating homeyness and respecting patients' agency; and promoting trustworthiness. CONCLUSION: Our findings suggest that-apart from the last theme, promoting trustworthiness, the rest are in line with Ulrich's supportive design theory. Therefore, further research is needed to investigate "promoting trustworthiness" in the context of cancer wards. In addition, each aspect of the visual design framework can offer practical design recommendations for future studies.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".