Creating Compassionate Spaces for End-of-Life Care for Older People Experiencing Homelessness: Protocol for an Environmental Assessment of Hospice Settings
Bibliographic record
Abstract
BACKGROUND: With current data supporting an increasing population of older people experiencing homelessness (OPEH) requiring unique spatial and placemaking considerations in end-of-life care, understanding the environmental factors that influence their well-being is crucial. OBJECTIVE: This protocol paper provides a comprehensive overview for evaluating hospice environments tailored to the needs of OPEH. METHODS: The Aging in the Right Place study aims to address this gap by developing and implementing the Aging in the Right Place-Hospice Environmental Assessment Protocol (AIRP-HEAP) and AIRP-HEAP secondary observation tools. The AIRP-HEAP tool evaluates the built and natural environment within hospice settings. Adaptations were made to ensure alignment with the unique needs of OPEH, such as reconceptualizing spiritual care and expanding the definition of family accommodation. Additionally, the AIRP-HEAP secondary observation tool supplements this by capturing contextual data on the surrounding neighborhood of the hospice site, providing a holistic understanding. RESULTS: Data were collected at Maggie's Lodge hospice between November and December 2024 using the AIRP-HEAP and AIRP-HEAP secondary observation tools. The dataset is currently being cleaned, with analysis planned between May and December 2025. The anticipated results will highlight the importance of considering environmental factors in hospice environments and inform recommendations to improve end-of-life care for OPEH. CONCLUSIONS: Data collected using these audit tools can guide environmental modifications in hospice settings to facilitate aging and end-of-life care in the right place. Thus, this protocol paper aims to promote the adoption of best practices in hospice design to better support this marginalized population. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/73356.
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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 | 0.044 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.010 |
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".