How to define and quantify a bad death in palliative home care? Across-sectional and exploratory study using Canadian interRAI data
Notice bibliographique
Résumé
BACKGROUND: Dying is a complex process comprised of physical, social, cultural, spiritual, environmental, and interpersonal relationship factors that contribute to both good and bad death experiences. Bad deaths have historically been explored with a qualitative lens. This study aimed to identify key indicators of a bad death and examine predictors for each indicator using population-level data. METHODS: This cross-sectional study analyzed routinely collected clinical and sociodemographic data using the Resident Assessment Instrument for Home Care (RAI-HC) between April 2007 and March 2020. 16,586 home care clients aged 18 years and older who died and had an assessment completed within 30 days of their death were included. Four indicators of a bad death were examined: self-reported loneliness, severe depressive symptoms, daily pain that is horrible or excruciating, and pain that is severe/excruciating and uncontrolled by medications. These indicators were interRAI specific variables that captured common bad death constructs in the existing literature. The study sample was separated into groups based on these four indicators and each individual could populate more than one group. Chi-square analyses were used to examine the relationship between potential risk factors and each bad death indicator. RESULTS: Of the total sample, 50.9% were 85 + years of age, and 54.7% were female. The prevalence of experiencing at least one of the bad death indicators was 33.5%. Each indicator significantly increased the likelihood of experiencing one of the other indicators with the ORs ranging from 1.70 to 3.26. Other important predictors that increased the odds of experiencing each bad death indicator included: any psychiatric diagnoses (OR range: 1.29-1.89), experiencing conflict with family or friends (OR range: 1.21-3.40), and a decline in social interaction which was distressing to the person (OR range: 2.06-3.70). CONCLUSIONS: These four bad death indicators were common among community-dwelling adults. This study found that there was an interconnectedness between the bad death indicators. Clinically, the relationship between these indicators means that addressing one aspect of a bad death may positively influence the others. Early identification of these issues, along with client and family collaboration, can aid in optimizing the likelihood of a good death.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».