Access to Palliative Care in the USA: Why Emphasize Vulnerable Populations?
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,003 | 0,007 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».