Orchestrating comfort: getting everyone on the same page: long term care nurses’ experiences with advance care planning
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".