Palliative care for people who use drugs during communicable disease epidemics and pandemics: A scoping review on access, policies, and programs and guidelines
Bibliographic record
Abstract
BACKGROUND: People who use drugs with life-limiting illnesses experience substantial barriers to accessing palliative care. Demand for palliative care is expected to increase during communicable disease epidemics and pandemics. Understanding how epidemics and pandemics affect palliative care for people who use drugs is important from a service delivery perspective and for reducing population health inequities. AIM: To explore what is known about communicable disease epidemics and pandemics, palliative care, and people who use drugs. DESIGN: Scoping review. DATA SOURCES: We searched six bibliographic databases from inception to April 2021 as well as the grey literature. We included English and French records about palliative care access, programs, and policies and guidelines for people ⩾18 years old who use drugs during communicable disease epidemics and pandemics. RESULTS: Forty-four articles were included in our analysis. We identified limited knowledge about palliative care for people who use drugs during epidemics and pandemics other than HIV/AIDS. Through our thematic synthesis of the records, we generated the following themes: enablers and barriers to access, organizational barriers, structural inequity, access to opioids and other psychoactive substances, and stigma. CONCLUSIONS: Our findings underscore the need for further research about how best to provide palliative care for people who use drugs during epidemics and pandemics. We suggest four ways that health systems can be better prepared to help alleviate the structural barriers that limit access as well as support the provision of high-quality palliative care during future epidemics and pandemics.
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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 | 0.016 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".