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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".