Impact of a multifaceted and multidisciplinary intervention on pain, agitation and delirium management in a Canadian community intensive care unit: a quality improvement study protocol
Bibliographic record
Abstract
BACKGROUND: Pain and agitation are closely linked to the development of delirium, which affects 60%-87% of critically ill patients. Delirium is associated with increased mortality and morbidity. Clinical guidelines that suggest routine assessment, treatment and prevention of pain, agitation and delirium (PAD) is crucial to improving patient outcomes. However, the adoption of and adherence to PAD guidelines remain suboptimal, especially in community hospitals. The aim of this quality improvement study is to evaluate the impact of a multifaceted and multidisciplinary intervention on PAD management in a Canadian community intensive care unit (ICU). METHODS: This is a quality improvement, uncontrolled, before-and-after study of a multifaceted and multidisciplinary intervention targeting nurses (educational modules, visual reminders), family members (interviews, educational pamphlets and an educational video), physicians (multidisciplinary round script) and the multidisciplinary team as a whole (delirium poster). We will collect data every day for 6 weeks before implementing the intervention. Data collection will include clinical information and information on process of care. We will then implement the intervention. Four weeks after, we will collect data daily for 6 weeks to evaluate the effect of the intervention. On the basis of the volume of the ICU, we expect to enroll approximately 280 patients. We have obtained local ethics approval from the Hamilton Integrated Research Ethics Board (HiREB 18-040-C). INTERPRETATION: The results of this quality improvement study will provide information on adherence to PAD guidelines in a Canadian community ICU setting. They will also supply information on the feasibility of implementing multifaceted and multidisciplinary PAD interventions in community ICUs.
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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 | 0.046 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".