Illicit drug use as a challenge to the delivery of end-of-life care services to homeless persons: Perceptions of health and social services professionals
Bibliographic record
Abstract
Homeless persons tend to die younger than the housed population and have complex, often unmet, end-of-life care needs. High levels of illicit drug use among this population are a particular challenge for health and social services professionals involved in end-of-life care services delivery. This article explores the challenges of end-of-life care services to homeless illicit drug users based on data collected during a national study on end-of-life care services delivery to homeless persons in Canada. The authors conducted qualitative interviews with 50 health and social services professionals involved in health services delivery to homeless persons in five cities. Interviews were transcribed verbatim and analysed thematically. Themes were organised into two domains. First, barriers preventing homeless illicit drug users from accessing end-of-life care services, such as competing priorities (e.g. withdrawal management), lack of trust in healthcare providers and discrimination. Second, challenges to end-of-life care services delivery to this population in health and social care settings, including non-disclosure of illicit drug use, pain and symptom management, interruptions in care, and lack of experience with addictions. The authors identify a need for increased research on the role of harm reduction in end-of-life care settings to address these challenges.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".