Design-Related Impacts on End-of-Life Experience: A Brief Report of Findings from an Exploratory Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Despite the general preference to die at home, many deaths occur in institutionalized settings. While biomedical interventions to ameliorate end-of-life (EoL) suffering have advanced, the end-of-life care (EoLC) environment is less understood as a means of palliative support. OBJECTIVE: This exploratory study considered the implications of clinical EoLC environments (facility buildings and their adjacent areas), aiming to understand how these designed spaces may be improved to better support experiences for patients, families, and staff. METHODS: Using an ethnography-driven approach, field observations (including participant commentaries) were captured at a standalone hospice and a palliative care ward at a general hospital. These were supplemented with semi-structured interviews. Content and thematic analyses were performed based on an interpretive-descriptive paradigm. Finally, informed by a review of field literature, analyses of all data were inter-related, and an interpretation was built to highlight key design considerations. RESULTS: as guiding concepts to appraise and improve such settings. CONCLUSION: Physical, emotional, and social wellbeing at the end of life is coalesced in and made visible by the designed environment. Therefore, evidence-based design serves as an important non-clinical intervention in such settings; however, patient involvement in such research remains difficult. Future scholarly research, new building schemes, and renovation projects should further examine the socio-spatial functions of clinical EoLC environments and investigate the challenges surrounding patient engagement within this domain.
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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 | 0.032 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".