Stigma about palliative care: origins and solutions
Bibliographic record
Abstract
Despite high-level evidence demonstrating the benefits of integrating palliative care early in the trajectory of advanced cancer, there remains a stigma surrounding this important discipline.This stigma is rooted in the origins of palliative care as care for the dying and is propagated by misinformation and late referrals to palliative care services.Current official definitions of palliative care emphasise the importance of early identification and treatment of symptoms and provision of care concurrently with treatments aimed at improving survival.However, this model of palliative care is neither widely known by patients and their caregivers nor consistently practiced.Herein, we describe changes that are necessary at the levels of practice, policy and public education to shift the status quo.Change requires palliative care teams that are staffed, trained and resourced to accommodate early referrals; education for referring physicians to provide high-quality primary palliative care, as well as timely referral to specialists; and a public health strategy for timely palliative care that educates and engages policymakers, stakeholders and the public.The hospice movement was directed at improving care for the dying; continued expansion of this movement is necessary so that all patients with advanced cancer may benefit from its principles throughout the course of illness.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.041 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 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".