Recommendations for improving the end-of-life care system for homeless populations: A qualitative study of the views of Canadian health and social services professionals
Bibliographic record
Abstract
BACKGROUND: Homeless populations have complex and diverse end-of-life care needs. However, they typically die outside of the end-of-life care system. To date, few studies have explored barriers to the end-of-life care system for homeless populations. This qualitative study involving health and social services professionals from across Canada sought to identify barriers to the end-of-life care system for homeless populations and generate recommendations to improve their access to end-of-life care. METHODS: Semi-structured qualitative interviews were conducted with 54 health and social services professionals involved in end-of-life care services delivery to homeless persons in six Canadian cities (Halifax, Hamilton, Ottawa, Thunder Bay, Toronto and Winnipeg). Participants included health administrators, physicians, nurses, social workers, harm reduction specialists, and outreach workers. Interviews were audio-recorded, transcribed verbatim and analysed thematically. RESULTS: Participants identified key barriers to end-of-life care services for homeless persons, including: (1) insufficient availability of end-of-life care services; (2) exclusionary operating procedures; and, (3) poor continuity of care. Participants identified recommendations that they felt had the potential to minimize these barriers, including: (1) adopting low-threshold strategies (e.g. flexible behavioural policies and harm reduction strategies); (2) linking with population-specific health and social care providers (e.g. emergency shelters); and, (3) strengthening population-specific training. CONCLUSIONS: Homeless persons may be underserved by the end-of-life care system as a result of barriers that they face to accessing end-of-life care services. Changes in the rules and regulations that reflect the health needs and circumstances of homeless persons and measures to improve continuity of care have the potential to increase equity in the end-of-life care system for this underserved population.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.030 | 0.017 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".