Optimizing the role of nurses in critical care in weaning patients from the ventilator : a multiple-case study
Bibliographic record
Abstract
Background: Prolonged mechanical ventilation is harmful for patients requiring prompt weaning approaches from an interprofessional team with different and overlapping scopes of practice. Nurses play a key role in interprofessional teams, and optimization of their role can reduce the duration of mechanical ventilation. Purpose: To understand the role of nurses in critical care in healthcare teams when weaning patients from mechanical ventilation. Methods: Multiple-case study with concurrent mixed methods data collection was conducted in two critical care units following a pilot study in Québec, Canada. A validated and adapted questionnaire, “Survey of Mechanical Ventilation and Weaning Role Responsibilities” was completed by nurses, respiratory therapists, and physicians (n = 102). Interviews (n=49) were conducted with content analysis completed. Descriptive statistics were generated for quantitative data. Rodgers et al. (2016) reporting standards for case studies were used. Results: Questionnaires showed that nurses had little involvement, autonomy, and influence over decisions related to weaning. However, in interviews, participants described several strategies used by nurses to support patients’ weaning from mechanical ventilation. Discussion: In the context of this research, the role of nurses in critical care is suboptimal when weaning patients from ventilators, however, in-depth knowledge of patient status, protocols, and education can support optimizing the nurse’s role in the weaning process. Conclusion: Strategies are needed to optimize the role of nurses in critical care when weaning patients from mechanical ventilation.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".