The Experience of Intensive Care Nurses Caring for Patients with Delirium
Bibliographic record
Abstract
The purpose of this research was to seek a deep understanding of the lived experience of intensive care nurses caring for patients with delirium. Delirium affects a large proportion of adult patients in the intensive care unit (ICU). Delirium has been linked to increased morbidity and mortality, longer intensive care and hospital length of stay, long-term cognitive impairments, short-term and long-term psychological distress, and increased hospital and health system costs. Critical care nurses play central roles in preventing, identifying, and treating ICU patients with delirium. Semi-structured interviews were conducted with eight intensive care nurses working in an ICU in a tertiary level, university-affiliated hospital in Ontario, Canada. The researcher analyzed the interviews using an interpretive phenomenological approach as described by van Manen (1990). The essence of the experience of critical care nurses caring for ICU patients with delirium was revealed to be finding a way to help them come through it. Six main themes emerged: It's Exhausting; Making a Picture of the Patient's Mental Status; Keeping Patients Safe: It's a Really Big Job; Everyone Is Unique; Riding It Out With Families; and Taking Every Experience With You. The findings describe how intensive care nurses find a way to help patients and their families through this complex and often distressing experience. This study has contributed to the understanding of the lived experience of ICU nurses caring for patients with delirium.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".