Critical care nurses management of prolonged weaning: an interview study
Bibliographic record
Abstract
BACKGROUND: For most critically ill patients requiring mechanical ventilation in the intensive care unit (ICU) weaning is uncomplicated. For the remainder, weaning is a challenge and may result in further complications and increased risk of mortality. Critical care nurses (CCNs) require substantial knowledge and experience to manage patients experiencing prolonged weaning. AIM: The aim of this study was to explore CCNs approach for management of patients experiencing prolonged weaning in the ICU. DESIGN: A descriptive qualitative design. METHODS: Semi-structured interviews with 19 experienced CCNs were conducted. Data were analysed using content analysis. RESULTS: Participants used various strategies for weaning that were grouped into four categories: individualized planning for the weaning process, assessing patient's capacity, managing the process and team interaction. The overall theme that emerged was: CCNs drive the weaning process using both a patient centred and targeted approach. CONCLUSION: CCNs in these ICUs performed weaning using a patient centred approach to plan, initiate, assess and establish a holistic weaning process. Team collaboration among all health care practitioners was important. CCNs have a key role in prioritizing weaning and driving the process forward. RELEVANCE TO CLINICAL PRACTICE: Closeness to the patient and a key role in management of the mechanical ventilated patient in ICU gives the CCN unique potential to develop and create models and tools for prolonged weaning. These tools, specially designed for patients undergoing prolonged weaning, would give focus on continuous planning, collaborating, managing and evaluation in the process of liberating patients from mechanical ventilation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".