COVID-19, promotion and provision of palliative care: reaching out, accounting for linguistic diversity
Bibliographic record
Abstract
The combined forces of economic globalization and international migration have resulted in specific challenges to palliative care systems. The COVID-19 pandemic has and is still greatly affecting elder populations as well as those across the age continuum living with long-standing chronic conditions or with pre-existing diverse unmet needs. While health promotion and palliative care may appear to be conceptually opposing fields, we argue that palliative care can and should fit under the umbrella of the health promotion continuum. This commentary seeks to discuss the importance of linguistic literacy and communication imperatives in the context of access to palliative care, given the broad, diversified and sensitive scope of care. While the pandemic has demonstrated that the public health responses of migrant host societies are deeply intertwined with policies as well as local rules and constraints, the promotion and provision of safe, timely and appropriate palliative care can be achieved through a sensitive assessment of differential contexts of diversity. The pandemic has painfully illustrated the need for a strong, respectful and equitable working partnership within the professions as well as with the civic society in order for the palliative needs of those exposed to a sustained risk not to be forgotten.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.034 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.012 |
| 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".