The Challenges of Advance Care Planning for Acute Care Registered Nurses
Bibliographic record
Abstract
STUDY BACKGROUND: The practice of acute care nurses is shaped by organizational factors such as lack of privacy, heavy workloads, unclear roles, lack of time, and lack of specific policies and procedures. We know little about the social and organizational structures and processes that influence nurses' uptake of valuable patient-centered discussions like advance care planning (ACP). ACP is beneficial for patients, their substitute decision makers, and healthcare providers. PURPOSE: To describe the operational, organizational, and societal influences shaping nurses' ACP work in acute care settings. METHODS: This ethnographic study purposively sampled 14 registered nurses and 9 administrators who worked in two acute care hospitals in Northeastern Ontario. Methods consisted of 23 open-ended, semi-structured interviews, 20 hours of observational fieldwork, and a collection of publicly available organizational documents. Data were inductively analyzed using an iterative constant comparative approach. RESULTS: Nurses were challenged to meet multiple competing demands, leaving them to scramble to manage complex and critically ill acute care patients while also fulfilling organizational tasks aligned with funding metrics, accreditation, and strategic planning priorities. Such factors limited nurses' capacity to engage their patients in ACP. CONCLUSIONS: Acute care settings that align patient values and medical treatment need to foster ACP practices by revising organizational policies and processes to support this outcome, analyzing the tasks of healthcare providers to determine who might best address it, and budgeting how to support it with additional resources.
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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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".