The intersections of palliative care and homelessness in social policy: A content analysis of Canadian policy documents
Bibliographic record
Abstract
Abstract Background Palliative care for people experiencing homelessness (PEH) is a social issue of increasing importance. Policymakers are best positioned to lead societal responses by naming the issue in policy documents, allocating resources to address palliative care for PEH, and creating frameworks or guiding principles to inform action. This study aims to examine how, if at all, policymakers in Canada are identifying and addressing the issue of palliative care for diverse PEH in policies and frameworks governing the palliative care and/or homelessness sectors. Methods We conducted a content analysis of 75 Canadian policy documents governing palliative care or homelessness for the presence of discussion of homelessness (in palliative care documents) and end-of-life (in homelessness documents). The level of discussion (no, indirect, minimal, significant), the jurisdictional level (municipal/city, provincial/territorial, national), and mention of intersecting identities were also recorded. Results Of the 75 documents analyzed, 42 contained no discussion of palliative care and homelessness, and only five contained significant discussions by explicitly identifying barriers, describing unique needs, and identifying competencies or innovative practices to promote access and inclusion. All significant or national level discussions were palliative care documents. Intersectional discussions of palliative care for PEH were found in 9 of 75 of documents, with ethnicity and Indigeneity mainly mentioned in palliative care documents, and older age and gender mentioned solely in homelessness documents. Conclusions There are critical gaps in Canadian policy documents governing palliative care and homelessness. Most policy documents fail to name or address the issues, with the gap most pronounced in homelessness documents, which contained no national level or significant discussions about end-of-life. Additionally, policy documents from both sectors seldomly discussed the unique needs and barriers of older, racialized, and/or gender-marginalized PEH at end-of-life. While competencies and service level solutions appear to be emerging within palliative care policies at the national level, policymakers from both sectors and across all levels of government must collaborate to address the unique needs of diverse PEH at end-of-life.
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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 | 0.027 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.027 | 0.043 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".