Bibliographic record
Abstract
OBJECTIVE: The purpose of this article is to identify how inclusive and accessible palliative care can be achieved for all, including those labelled as vulnerable populations. METHOD: Drawing on a review of existing literature as well the research of the Vulnerable Persons and End-of-Life New Emerging Team (VP-NET), this article reflects on what changes can be made within palliative care to make it more inclusive. RESULTS: Experiences of marginalization often result, intentionally or unintentionally, in differential treatment in healthcare. This increased vulnerability may result from attitudes of healthcare providers or from barriers as a result of "normal" care practices and policies that may exclude or stigmatize certain populations. This may include identifying when palliative care is necessary, who receives palliative care and where, and what is necessary to complement palliative care. SIGNIFICANCE OF RESULTS: Inclusive and accessible palliative care can become possible through building on the existing strengths in palliative care, as well as addressing existing barriers. This may include treating the whole person and that person's support team, including paid support workers, as part of the unit of care. It involves ensuring physically accessible hospice and palliative care locations, as well as thinking creatively about how to include those excluded in traditional locations. Inclusive palliative care also ensures coordination with other care services. Addressing the barriers to access, and inclusion of those who have been excluded within existing palliative care services, will ensure better palliative and end-of-life care for everyone.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".