Patients' and nurses' experiences of delirium: a review of qualitative studies
Bibliographic record
Abstract
BACKGROUND: Knowledge of delirium accumulated over the past two decades has focused more on its characteristics, pathophysiology, incidence, aetiology and prognosis as well as interventions for preventing, detecting, evaluating or managing this syndrome and less so on how patients and nurses who care for them experience it. AIMS: To present the state of knowledge derived from qualitative studies of the experiences of persons who suffered delirium and of nurses who cared for them to guide critical care practice. RESULTS: Delirious patients experience incomprehension and various feelings of discomfort. Understanding, support, believing what they are experiencing, explanations, the presence of family/friends and the possibility of talking about the lived experience are interventions that might help them get through such episodes more easily. Nurses who tend to delirious patients fail to comprehend the utterances and behaviours of the persons cared for and experience various feelings of discomfort as well. Nevertheless, they intervene following different goals and intervention strategies that seem to vary as a function of their culture and values. CONCLUSION: Qualitative studies conducted on persons who suffered delirium and on nurses who cared for them have shed light on their lived experience and provide insight on how to improve critical care practice. RELEVANCE TO CLINICAL PRACTICE: The findings suggest that nurses must acknowledge the lived experience of the persons cared for and they must seek out the meaning that patients ascribe to this experience to understand the situation and thus conduct interventions that meet the needs expressed.
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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 | 0.025 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".