Factors influencing nurses’ use of sedation interruptions in a critical care unit: a descriptive qualitative study
Bibliographic record
Abstract
INTRODUCTION AND AIMS: This study examined critical care nurses', physicians', and allied health professionals' perceptions of factors that support, inhibit, or limit the use of sedation interruption (SI) to improve the use of this integral component of care for mechanically ventilated patients. METHOD: We conducted a theory-based, descriptive qualitative study using semi-structured interviews with critical care registered nurses, respiratory therapists, a pharmacist, and a physician in a hospital in Ontario, Canada. The interview guide and analysis were informed by the Theoretical Domains Framework and transcripts were analyzed using content analysis. RESULTS: We identified 9 facilitators and 20 barriers to SI use by nurses. Facilitators included the innovation (importance of protocols) and potential adopters (comfort with the skill). The barriers were the potential adopters' (nurses) knowledge gaps regarding the performance and goal of SI and the practice environment (lack of time, availability of extra staff, and lack of multidisciplinary rounds). CONCLUSION: This study identified facilitators and barriers to SI for mechanically ventilated patients. Implementation efforts must address barriers associated with nurses, the environment, and contextual factors. A team-based approach is essential, as the absence of interprofessional rounds is a significant barrier to the appropriate use or non-use of SI. Future research can focus on the indications, contraindications, and goals of SI, emphasizing a shared appreciation for these factors across disciplines. Nursing capacity to manage a patient waking up from sedation is necessary for point-of-care adherence; future research should focus on the best ways to do so. Implementation study designs should use theory and evidence-based determinants of SI to bridge the evidence-to-practice gap. SPANISH ABSTRACT: http://links.lww.com/IJEBH/A178.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".