Lost in (Knowledge) Translation!
Bibliographic record
Abstract
Critical care nutrition guidelines have been developed to help busy practitioners decide how to feed their critically ill patients. However, despite the publication of guidelines and efforts to disseminate and implement them, there are large gaps between what the recommendations say and what is happening at the bedside. Consequently, the nutrition therapy received by many patients remains suboptimal. Knowledge translation is a term increasingly used in healthcare to describe the process of moving evidence learned from clinical research and summarized in clinical practice guidelines to incorporation into clinical and policy decision making. In this article, knowledge about the implementation of critical care nutrition guidelines is applied to Graham et al's knowledge-to-action model to illuminate the issues pertinent to knowledge translation in critical care nutrition. This model has 2 components: knowledge creation and action. The action component consists of 8 phases of the action cycle that represent activities needed to move knowledge into practice and are derived from planned-action theory. Components of this model are illustrated via empirically derived research, commentaries, and published studies from the field of critical care nutrition. It is hoped that this article and related articles in this issue of JPEN will help critical care nutrition practitioners to better understand the often complex and convoluted road of translating knowledge into practice so that as a community we are no longer "lost" but have direction that can bring about positive changes in 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 | 0.063 | 0.283 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.019 | 0.038 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.033 | 0.017 |
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".