Access to Palliative Care in the USA: Why Emphasize Vulnerable Populations?
Bibliographic record
Abstract
How we recognize and address the palliative care needs of marginalized, vulnerable, or socially excluded populations may very well be the measure of our success in integrating palliative care into current health systems. LuAnn Aday, who has written about access for under-served populations in the USA, defines vulnerable populations as being ‘at risk of poor physical, psychological, and/or social health’1. If we deconstruct her definition, being ‘at risk’ signifies a high probability of illness and inappropriate or no healthcare, and the possible causes include lack of access to preventive services, disease screening and treatment as well as lack of access to the housing, employment and other supportive structures that many of us take for granted. Aday then identifies some of the at-risk populations in the USA—the chronically ill and disabled, persons with AIDS, the mentally ill, alcohol and substance abusers, homeless people, immigrants and refugees and combinations of these1. We would add another category that crosses this list—membership of a minority or ethnic community. Because of our history of discrimination and unequal investment in health, social, and economic resources in these communities, they are disproportionately at risk. We already have some indication of differences among vulnerable populations in their knowledge of end-of-life care. For example, Silveira and DiPiero2 identified non-white race and low educational level as factors associated with lack of such knowledge and Shapiro et al.3, in the HIV Cost and Services Utilization Study (HCSUS), found differences in use of medical care and pharmaceuticals by race, gender, and insurance status. Another study followed Medicare patients admitted to hospital in three American States (Illinois, New York, and Pennsylvania) with principal diagnoses of congestive heart failure and pneumonia4; on a series of measures, black patients were found to receive lower-quality care than non-black patients, and these differences persisted when groups with equivalent poverty levels were compared. In the USA, lack of insurance may complicate access. But data from other countries indicate that universal health coverage does not always result in universal access. For example, a Canadian study found that access to cardiac services decreased, and mortality rates after acute myocardial infarction increased, by income grouping: the lower the income quintile, the more adverse the health consequences5. We know which populations are at high risks of illness and we have studied the barriers to their obtaining good-quality healthcare. In our palliative care planning we need to discuss how to lessen the chances of excluding them as we integrate palliative care with primary and specialty healthcare and end-of-life care such as hospice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".