Optimizing Nutrition in Intensive Care Units: Empowering Critical Care Nurses to Be Effective Agents of Change
Bibliographic record
Abstract
Observational studies have consistently revealed wide variation in nutritional practices across intensive care units and indicated that the provision of adequate nutrition to critically ill patients is suboptimal. To date, the potential role of critical care nurses in implementing nutritional guideline recommendations and improving nutritional therapy has received little consideration. Factors that influence nurses' nutritional practices include the lack of guidelines or conflicting evidence-based recommendations pertaining to nurses' practice, strategies for implementing guidelines that are not tailored to barriers nurses face when feeding patients, strategies to communicate best evidence that do not capitalize on nurses' preference for seeking information through social interaction, prioritization of nutrition in initial and continuing nursing education, and a lack of interdisciplinary team collaboration in the intensive care unit when decisions on how to feed patients are made. Future research and quality improvement strategies are required to correct these deficits and successfully empower nurses to become nutritional champions at the bedside. Using nurses as agents of change will help standardize nutritional practices and ensure that critically ill patients are optimally fed.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".