Orchestrating comfort: getting everyone on the same page: long term care nurses’ experiences with advance care planning
Notice bibliographique
Résumé
Background: The majority of residents in long term care (LTC) facilities are older and frail, with multiple comorbidities and reduced cognitive capacity. Although the evidence suggests that advance care planning (ACP) improves the quality of end-of-life (EOL) care and promotes a good death for residents of LTC, such planning rarely occurs in these settings. Moreover, while nurses are in the ideal position to facilitate ACP, there is a paucity of empirical research examining their engagement in ACP. Purpose: The purpose of this qualitative study was to develop an inductively derived empirical model aimed at understanding the experiences of nurses working in LTC facilities, specifically with regard to their engagement in the ACP process. Design: A constructivist grounded theory (CGT) methodology was used to conduct this study. Symbolic interactionism (SI) and the socio-ecological model (SEM) served as sensitizing theoretical perspectives for this study. Purposive and theoretical sampling were used to recruit 25 registered nurses (RNs) from 18 proprietary and non-proprietary LTC facilities in Winnipeg, Manitoba who had worked a minimum of three months in LTC, were able to read/speak English, and were willing to provide consent to participate in the study. Methods: Data were collected using a demographic questionnaire; in-depth, semi-structured, audio-recorded, face-to-face/telephone interviews; field notes; and memos. Demographic data were analyzed with descriptive statistics. Verbatim transcriptions of the interviews were analyzed with specific CGT coding procedures. Findings: The basic social problem that emerged from the data was that of nurses trying to craft and implement an ACP level that they believed would optimize residents’ comfort in LTC. The empirically derived theoretical model that captured the experiences, processes, and strategies of nurses trying to address the identified social problem was orchestrating comfort: getting everyone on the same page. This model encompassed two main processes, downgrading and upgrading ACP levels, and two pre-conditions, piecing together the big picture and selling the big picture. The nurses were able to maximize residents’ comfort at EOL and during acute events by downgrading and upgrading ACP levels, respectively. The nurses believed that a universal understanding of the residents’ condition would lead to a realistic ACP level that would, in turn, optimize comfort. The nurses identified several facilitators and barriers at the resident/family, healthcare provider, and organizational levels for the processes of downgrading and upgrading ACP levels. Several positive and negative consequences of orchestrating comfort at the resident, family, and nurse levels were also noted in this study. Conclusion: This study fills an important gap in the literature by explicating the ways in which LTC nurses engage in ACP as well as the factors that facilitate or constrain their ability to optimize resident comfort. It was the first Canadian study to illustrate the micro- and macro-perspectives of ACP through the dual lens of SEM and SI. A multitude of implications for the healthcare system and future research arose from this study, specifically with regard to practice, education, research, and policy development.
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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 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,008 | 0,006 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,008 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».