ArticleATS scholar2022
Development and Evaluation of an Intensive Care Unit Video Series to Educate Staff on Delirium Detection.
Article in ATS scholar, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07759310 (Evaluation of the Effectiveness of the ICU Delirium Playbook in Nursing Education), which is not on this map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Evaluation of the Effectiveness of the ICU Delirium Playbook in Nursing Education: A Pre-Post Study in the ICUs of the Pisa University Hospital
Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Video-based Detection of Delirium in Hospitalized Adults.medRxiv : the preprint server for health sciences · 2026Article
- Video-based detection of Delirium in hospitalized adults.PLOS digital health · 2026Article
- Evaluating the Effectiveness of Educational Intervention on ICU Nurses' Knowledge of Delirium: A Quasi-Experimental Approach.Nursing reports (Pavia, Italy) · 2025Article
- Nurses' Experiences of the Quality of Family-Centered Patient Counselling in Paediatric Delirium: A Qualitative Descriptive Study.Nursing open · 2025Article
- Development and validation of nurse's assessment ability questionnaire in delirium subtypes: Based on Delphi expert consensus.PloS one · 2024Article
- Development and Validation of an ICU Delirium Playbook for Provider Education.Critical care explorations · 2023Article
- Delirium in the United States: Results From the 2023 Cross-Sectional World Delirium Awareness Day Prevalence Study.Journal of the Academy of Consultation-Liaison PsychiatryArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 7 institutions in 2 countries.
Funding
Abstract
Background: Delirium affects up to 80% of patients who are mechanically ventilated in the intensive care unit (ICU) but often goes undetected because of incomplete and/or inaccurate clinician evaluation and documentation. A lack of effective, feasible, and sustainable educational methods represents a key barrier to efforts to optimize, scale, and sustain delirium detection competencies. Progress with such barriers may be addressed with asynchronous video-based education. Objective: To evaluate a novel ICU Delirium Video Series for bedside providers via a knowledge assessment quiz and a feedback questionnaire. Methods: An interdisciplinary team scripted and filmed an educational ICU Delirium Video Series, providing detailed instruction on delirium detection using the validated CAM-ICU (Confusion Assessment Method for the ICU). A cohort of bedside nurses subsequently viewed and evaluated the ICU Delirium Video Series using a feedback questionnaire and a previously developed knowledge assessment quiz pre- and post-video viewing. Results: Twenty nurses from four ICUs viewed the ICU Delirium Video Series and completed the pre-post quiz and questionnaire. Ten (50%) respondents had 10 or more years of ICU experience, and seven (35%) reported receiving no CAM-ICU education locally. After video viewing, overall pre-post scores improved significantly (66% vs. 79%; Conclusion: Viewing the ICU Delirium Video Series resulted in significant improvements in knowledge and yielded valuable feedback. Asynchronous video-based delirium education can improve knowledge surrounding a key bedside competency.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.