Delirium Around the World: Performing An International Point Prevalence Study Increases Awareness for Delirium
Bibliographic record
Abstract
Background Global efforts to raise delirium awareness have been made over the past years, such as the formation of the iDelirium group and the institution, in 2017, of the annual World Delirium Awareness Day (WDAD) in March. In 2023, our group and researchers from around the world conducted a global one-day point prevalence study of delirium on WDAD. These strategies appear to improve delirium recognition and management. Objective To assess participants’ perceived impact of participating in the WDAD 2023 one-day point prevalence study and to gather feedback on the survey process to inform future campaigns. Methods An online survey was administered to multiple national and clinical collaborators from 44 countries participating in the one-day global point delirium prevalence study on WDAD 2023. Using SurveyMonkey®, the survey was distributed via a snowball system, with participants’ consent implied through their involvement. The survey, developed iteratively to balance time and detail, consisted of six core questions, including closed and open formats. Data were collected from September 9th to October 9th, 2023, and qualitative and quantitative analyses were performed using Excel®. Quantitative data underwent statistical analysis using IBM® SPSS Statistics28. Results 49 out of 53 (93.45%) healthcare professionals responded to the survey: 71.43% reported increased awareness, and responses were almost equally divided between those who initiated projects (46.94%) and those who did not (53.06%). Most participants (93.88%) did not find the WDAD 2023 one-day point prevalence study in March burdensome, and all expressed interest in future participation. The WDAD 2023 one-day point prevalence study highlighted diverse projects, primarily focused on delirium awareness and education. The survey found suggestions for future improvements emphasizing more efficient, digital, and engaging research methods and a clear need for educational resources, particularly in ICU delirium. Conclusions Our survey finds that participation in WDAD 2023 one-day point prevalence study boosted delirium visibility, leading to ongoing awareness, interest in future studies, and practice changes that benefit patients and the healthcare system. Further evidence-based research is needed to assess the impact of these campaigns on delirium recognition and management.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".