Lessons Learned From Implementing a Novel Feeding Protocol
Bibliographic record
Abstract
Background: This study describes the results of an evaluation of educational strategies used to implement a novel enteral feeding protocol in 9 North American intensive care units (ICUs). Materials and Methods: Members of the protocol implementation teams at each ICU distributed a questionnaire to ICU nurses after the implementation of the Enhanced Protein-Energy Provision via the Enteral Route Feeding Protocol in Critically Ill Patients (PEP uP) protocol. Eight different educational strategies were evaluated. Questionnaires were distributed in both paper and electronic format to all nursing staff and used both a visual analog Likert-type scale and open-ended questions. Results: The response rate to the questionnaire was 166 of 434 or 38.2%. More than 70% of respondents rated 5 of the educational strategies as very useful or somewhat useful, including the long PowerPoint presentation at in-services and critical care rounds, the short PowerPoint presentation for 1-on-1 and group bedside teaching, and a self-learning module. The percentage of nurses who found the bedside protocol tools of the enteral feeding order set, gastric feeding flowchart, and volume-based feeding schedule either "very easy" or "somewhat easy" to use were 64.0%, 60.5%, and 59.1%, respectively. Conclusion: The use of multiple teaching formats, including the long and short PowerPoint presentations and self-teaching module, appeared to meet the learning needs of most of the group. The majority of the bedside tools developed to facilitate the implementation of the PEP uP protocol were considered easy to use.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".