A Flipped Classroom Approach to Improving the Quality of Delirium Care Using an Interprofessional Train-the-Trainer Program
Bibliographic record
Abstract
INTRODUCTION: Given the prevalence and morbidity associated with delirium, there is a need for effective and efficient institutional approaches to delirium training in health care settings. Novel education methods, specifically the "flipped classroom" (FC) and "train-the-trainer" (TTT), have the potential to address these delirium training gaps. This study evaluates the effect of a TTT FC interprofessional delirium training program on participants' perceived ability to manage delirium, delirium knowledge, and clinicians' delirium assessment behaviors. METHODS: FC Delirium TTT sessions were implemented in a large four-hospital network and consisted of presession online work and a 3-hour in-session component. The 156 TTT interprofessional participants who attended the sessions (ie, trainers) were expected to then deliver delirium training to their patient care units. Delirium care self-efficacy and knowledge test scores were measured before, after, and 6 months after the training session. Clinician delirium assessment rates were measured by chart audits before and 3 months after trainer's implementation of delirium training sessions. RESULTS: Delirium knowledge test scores (7.8 ± 1.6 versus 9.7 ± 1.2, P < .001) and delirium care self-efficacy were significantly higher immediately after the TTT session compared with those of presession and these differences remained significant at 6-month after the TTT session. Trainer sessions significantly improved clinician delirium assessment rates from 53% for pretraining to 66% for posttraining. DISCUSSION: Our data suggest that a TTT FC delirium training approach can improve participants' perceived delirium care skills and confidence, and delirium knowledge up to 6 months after the session. This approach provides a model for implementing hospitalwide delirium education that can change delirium assessment behavior while minimizing time and personnel requirements.
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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 | 0.004 | 0.008 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".