Implementation of a nurse-driven sedation protocol in the ICU.
Bibliographic record
Abstract
BACKGROUND: Managing anxiety, pain and delirium in critically ill patients is an ongoing challenge. Differences in physician practice, variations of pharmacological agents, as well as concentrations and units can increase the risk of medication error Personal preferences, subjectivity, and nurses' level of expertise are variables when titrating analgesic and sedation infusions. PURPOSE: The purpdse of this study was to evaluate the perceived benefits of implementing a standardized nurse-driven sedation protocol in the ICU. We examined its impact on the rates of medication errors and perceptions of staff using the protocol. DESIGN: This descriptive study used a survey to collect data. SAMPLE: We used a convenience sample of 75 nurses who worked in the ICU during the implementation of the sedation protocol. RESULTS: Analysis of variance was completed comparing all sub-scale scores. No statistical significance was found, but scores did not decrease over time. No medication errors or near misses were reported throughout the sedation protocol implementation. Qualitative comments from staff provided feedback and assisted in identifying issues with the protocol. CONCLUSION: We believe that the implementation of the sedation protocol has been beneficial in our adult ICU. Findings indicate that with experience and resources nurses can manage anxiety, pain and delirium more confidently than without such a protocol. Critical care nurses, given the right tools, education, and support can make decisions that promote positive outcomes for patients receiving sedation and analgesia in the ICU.
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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 | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".