Improving the Provision of Enteral Nutrition in the Intensive Care Unit
Bibliographic record
Abstract
BACKGROUND: Tailoring interventions to address identified barriers to change may be an effective strategy to implement guidelines and improve practice. The purpose of this article is to describe the development and implementation of a tailored intervention to overcome barriers to enterally feeding critically ill patients. METHODS: A before-after study was conducted in 5 hospitals in North America. We adopted a pragmatic stepwise approach to developing and implementing a tailored intervention-namely, (1) formation of a guideline implementation team, (2) identification of barriers to the provision of enteral nutrition (ie, guideline-practice gap analysis, staff survey, focus group with key stakeholders), (3) focus group to prioritize these barriers, (4) brainstorming to select interventions to overcome the prioritized barriers, (5) a 12-month implementation phase including bimonthly progress meetings, and (6) evaluation of the tailored intervention. RESULTS: All sites identified and prioritized barriers to target for change and developed a tailored action plan. Three of the 22 potential barriers were prioritized by all sites, resulting in common components to the action plans. However, barriers and interventions that were unique to specific sites were also identified. All sites were successful in implementing most of the selected strategies during the implementation phase, although the degree of implementation varied depending on the type of strategy and the site. CONCLUSION: This stepwise process to developing and implementing an intervention tailored to barriers is promising and could be considered by dietitians and other providers seeking to improve nutrition practice.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".