Using relational ethics to approach equity in palliative care
Bibliographic record
Abstract
Evidence suggests that people experiencing inequities and who are highly marginalized (e.g., people impacted by racism, sexism, discrimination, stigma, mental illness, substance use issues, disability, and the effects of homelessness; also referred to as structurally vulnerable individuals) often die alone, in pain, and not receiving the care they need. Some research even points to highly marginalized people not feeling worthy of care. The need to consider equity in the context of palliative care has recently emerged but little attention has been paid to how ethical decision-making generally, and relational ethics, specifically, could provide guidance in the care of highly marginalized people who are on a palliative trajectory. Relational ethics offers a model of care and decision-making framework that emphasizes how clients, healthcare providers, and larger social structures are interwoven and acknowledge that structural conditions can position people to have less choice than others. Relational approaches in the context of palliative care for highly marginalized people have the potential to provide a lens to better support the delivery of equitable palliative care. This critical essay explores relational ethics as a way to approach equity in palliative care to support clients facing structural vulnerabilities. We discuss relational ethical considerations to approach collaborative partnerships between clients, healthcare providers, and the larger community with the goal of aligning care with clients' values. An ethical case for how a relational ethics approach might be used to promote equitable access to palliative care will be explored, highlighting how such approaches have the potential to better align client wishes with their needs and to ensure decision-making and care delivery is trauma-informed, harm reduction focused, and culturally respectful. Relational ethics can support social change in equity and palliative care by contributing ethically informed ways of caring for/with/about highly marginalized people.
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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 | 0.030 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.082 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.011 |
| 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".