Nurses’ perceptions of quality end‐of‐life care on an acute medical ward
Bibliographic record
Abstract
AIM: This paper reports the findings of a study that generated a conceptual model of the nursing behaviours and social processes inherent in the provision of quality end-of-life care from the perspective of nurses working in an acute care setting. BACKGROUND: The majority of research examining the issue of quality end-of-life care has focused on the perspectives of patients, family members and physicians. The perspective of nurses has generally received minimal research attention, with the exception of those working within palliative or critical care. The vast majority of hospitalized patients, however, continue to be cared for and die on medical units. To date, little research has been conducted examining definitions and determinants of quality end-of-life care from the perspective of nurses working in acute adult medical settings. METHOD: Grounded theory method was used in this study of 10 nurses working on acute medical units at two tertiary university-affiliated hospitals in central Canada. Data were collected during 2002 by interview and participant observation. FINDINGS: The basic social problem uncovered in the data was that of nurses striving to provide high quality end-of-life care on an acute medical unit while being pulled in all directions. The unifying theme of 'Creating a haven for safe passage' integrated the major sub-processes into the key analytic model in this study. 'Creating a haven for safe passage' represents a continuum of behaviours and strategies, and includes the sub-processes of 'facilitating and maintain a lane change'; 'getting what's needed'; 'being there'; and 'manipulating the care environment'. CONCLUSION: The ability of nurses to provide quality end-of-life care on an acute medical unit is a complex process involving many factors related to the patient, family, healthcare providers and the context in which the provision of end-of-life care takes place.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".