Exploring the quality of dying of patients with chronic obstructive pulmonary disease in the intensive care unit: a mixed methods study
Bibliographic record
Abstract
RATIONALE FOR THE STUDY: Improving the quality of end-of-life (EOL) care in critical care settings is a high priority. Patients with advanced chronic obstructive pulmonary disease (COPD) are frequently admitted to and die in critical care units. To date, there has been little research examining the quality of EOL care for this unique subpopulation of critical care patients. AIMS: The aims of this study were (a) to examine critical care clinician perspectives on the quality of dying of patients with COPD and (b) to compare nurse ratings of the quality of dying and death between patients with COPD with those who died from other illnesses in critical care settings. DESIGN AND SAMPLE: A sequential mixed method design was used. Three focus groups provided data describing the EOL care provided to patients with COPD dying in the intensive care unit (ICU). Nurses caring for patients who died in the ICU completed a previously validated, cross-sectional survey (Quality of Dying and Death) rating the quality of dying for 103 patients. DATA ANALYSIS: Thematic analysis was used to analyse the focus group data. Total and item scores for 34 patients who had died in the ICU with COPD were compared with those for 69 patients who died from other causes. RESULTS: Three primary themes emerged from the qualitative data are as follows: managing difficult symptoms, questioning the appropriateness of care and establishing care priorities. Ratings for the quality of dying were significantly lower for patients with COPD than for those who died from other causes on several survey items, including dyspnoea, anxiety and the belief that the patient had been kept alive too long. The qualitative data allowed for in-depth explication of the survey results. CONCLUSIONS: Attention to the management of dyspnoea, anxiety and treatment decision-making are priority concerns when providing EOL care in the ICU to patients with COPD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".