Selection of knowledge translation strategies to implement best practices on nonpharmacological prevention of delirium in intensive care unit
Bibliographic record
Abstract
Background: Delirium prevention in the ICU should focus on a non-pharmacological approach. However, these recommendations are not always applied by care providers. Objective: To select knowledge translation strategies to facilitate the implementation of non-pharmacological best practices to prevent delirium in the ICU. Method: A consensus study was conducted. Barriers and facilitators to the implementation of nonpharmacological methods, and knowledge translation strategies, were identified in two nominal groups. A context assessment was also carried out. Nine professionals and one patient-partner participated. Results: The barriers and facilitators on which consensus was reached were most frequently related to environmental context and resources, intention, and knowledge. The areas of organizational context with the highest levels of agreement were interpersonal relations, culture and leadership. Consequently, knowledge translation strategies were selected to facilitate practices, as well as to modify the environment and improve knowledge. Conclusion: A structured method was used during this study to guide the selection of knowledge translation strategies. The application of these strategies could potentially improve clinical practice in intensive care.
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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 | 0.054 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".