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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".