Exploring the reassessment of palliative home care clients: A mixed methods study
Notice bibliographique
Résumé
Introduction: The timely reassessment of clients receiving palliative home care (HC) is critical for supporting clinicians with care planning and service delivery. However, there is little information on the frequency of reassessments and factors driving reassessment in palliative HC clients. Objectives: The objectives of this study were to: (i) determine the proportion of palliative HC clients reassessed with the interRAI Palliative Care (interRAI PC) instrument, (ii) calculate the average interRAI PC reassessment interval, (iii) identify the key factors driving interRAI PC reassessment at various intervals, and (iv) explore the factors that influence palliative HC clinicians to reassess their clients in general. Methods: This sequential mixed methods study was comprised of three phases: a quantitative phase (i.e., Phase I), a qualitative phase (i.e., Phase II), and a supplementary quantitative phase (i.e., Phase III). Phase I was a retrospective cohort study using secondary interRAI PC assessment data for palliative HC clients assessed in Ontario from 2011 to 2022 (n = 128,740). Clinically meaningful differences between clients who were not reassessed and those reassessed at four intervals (i.e., within 90 days, 91-180 days, 181-365 days, and beyond 365 days) were identified using absolute standardized differences. A standardized difference of 0.2 or greater represented at least a small effect size and was considered to denote a significant difference. Phase II followed a collective exploratory case approach with palliative HC clinicians (i.e., nurses and nurse practitioners) being recruited from two provinces in Canada (n = 3). Clinicians were recruited through a snowball convenience sampling approach. Background surveys and semi-structured interviews were conducted to gain a deeper understanding of the factors that influence their decision to complete reassessments. The interviews were transcribed verbatim and analyzed using a cross-case synthesis and Braun & Clarke’s steps for reflexive thematic analysis. Phase III followed the same design as the first phase of the study and was used to address additional predictors of interRAI PC reassessment based on the results of Phase II. Results: In Phase I, only 30.5% of the sample had a recorded reassessment, with the average reassessment interval being 198 days (standard deviation = 156). Across all comparisons, clients were significantly more likely to be reassessed if they had a prognosis of 6 months or longer, no/moderate health instability, no/mild levels of functional impairment, and/or a low/mild risk of developing a pressure ulcer. In Phase II, the researcher generated two main themes through her analysis: individualized care plans and a connected care team. In Phase III, clients with independent locomotion (i.e., walking or wheeling) were more likely to receive a reassessment at any interval. Conclusion: The timely reassessment of palliative HC clients is critical to ensure their changing care needs are identified and they are receiving the proper supports and resources. While this work identified several factors influencing reassessment, further research needs to be conducted to gain a deeper understanding of how the different predictors interact with one another.
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 | 0,030 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».