Advance Care Planning between Registered Nurses and their Acute Care Patients
Bibliographic record
Abstract
Canadians are living longer with multiple complex illnesses. In turn, older adults are often in need of complex medical attention in crisis situations in acute care hospital settings. Although acute care settings are equipped with a growing variety of life saving technologies, hospitals are still the setting in which most people die. Yet, almost half of the Canadians who have been admitted to acute care centres with chronic life-limiting illnesses have not had advance care planning (ACP) conversations with their substitute decision-maker (SDM) about the personal values that bring quality to their lives. In fact, only 8% of the general Canadian population are ACP ready. Consequently, many SDMs are unprepared to make end of life (EOL) treatment decisions for their loved ones. One way to promote patient-centred care and ease the burden of in-the-moment EOL treatment decisions made by SDMs, is for nurses to engage their patients in ACP. However, very few registered nurses regularly engage their patients in ACP. The purpose of this research is to better understand the organizational factors influencing nurses’ decisions related to ACP in their hospital-based work. This ethnographic study was conducted on three acute care wards in two hospital sites located in Northern Ontario. Data collection methods included observational fieldwork, semi-structured interviews with administrators and registered nurses (n=23), and the collection of documents pertinent to the study purpose (i.e., accreditation reports, practice guidelines, etc.). Findings reveal that the work of nurses in hospital settings is embedded within a context that prioritizes patient flow, and efficiency. Consequently, hospitals often function at overcapacity, and nurses have extremely heavy workloads caring for complex patients with diagnoses that do not match the medical specialty of the units. Although participants state that they value ACP, they maintain that nurses have very little capacity to engage patients in these conversations in their practice. Findings support that expectations for hospital nurses to fully engage in ACP with their patients may be unrealistic given the context within which they work. Alternative models for considering ACP in acute care could be explored to ensure that patients with life-limiting conditions receive care that is best matched to their needs, values, and wishes.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".