A first voice perspective of people experiencing homelessness on preferences for the end-of-life and end-of-life care during the COVID-19 pandemic
Bibliographic record
Abstract
OBJECTIVE: People experiencing homelessness often encounter progressive illness(es) earlier and are at increased risk of mortality compared to the housed population. There are limited resources available to serve this population at the end-of-life (EOL). The purpose of this study was to gain insight into preferences for the EOL and end-of-life care for people experiencing homelessness. Utilizing an interpretive phenomenology methodology and the theoretical lens of critical social theory, we present results from 3 participants interviewed from August to October 2020, with current or previous experience of homelessness and a diagnosis of advanced disease/progressive life-threatening illness. RESULTS: A key finding focused on the existential struggle experienced by the participants in that they did not care if they lived or died. The participants described dying alone as a bad or undignified way to die and instead valued an EOL experience that was without suffering, surrounded by those who love them, and in a familiar place, wherever that may be. This study serves to highlight the need for improvements to meet the health care and social justice needs of people experiencing homelessness by ensuring equitable, humanistic health and end-of-life care, particularly during the context of the COVID-19 pandemic.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| 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".