Evaluation of an Intervention With Nurses for Delirium Detection After Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Completion of a delirium detection tool allows rapid management, which alleviates complications. However, these tools are often underused. AIMS: To assess the effect of a knowledge transfer (KT) intervention on the completion of a delirium detection tool by nurses working with cardiac surgery patients. Secondary aims included describing completion rates per work shift, and patient characteristics associated with higher rates. METHODS: In a pre-post study, the intervention included a survey and focus groups to identify barriers to use of a delirium detection tool (Intensive Care Delirium Screening Checklist [ICDSC]). Nurses' suggestions for a KT activity and its implementation were also included. Using chi-square analysis and medical charts from 242 patients, we compared the pre- and postintervention rates of completion of the ICDSC. RESULTS: The majority of nurses who completed the survey (n = 30) felt they had the knowledge, skills, and intention to complete the ICDSC. During the focus groups (n = 4), a need for information on delirium symptoms and its management was raised as a barrier. This barrier was addressed with the selected KT activity (clinical capsule and aide-memoire handed out to nurses [n = 24]). Across all work shifts, the completion rate was similar pre- and postintervention. Overall, the completion rate was lower during the day shift than the night and evening shifts. A higher rate was associated with the first three postoperative days, and longer hospital and intensive care unit stays. LINKING EVIDENCE TO ACTION: A tailored intervention based on preidentified barriers and facilitators, using the Determinants of Implementation Behavior Questionnaire, and in collaboration with participants, has the potential to promote evidence-based 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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".