Interprofessional education for delirium care: a systematic review
Bibliographic record
Abstract
Recent delirium prevention and treatment guidelines recommend the use of an interprofessional team trained and competent in delirium care. We conducted a systematic review to identify the evidence for the value of interprofessional delirium education programs on learning outcomes. We searched several databases and the grey literature. Studies describing an education intervention, involving two or more healthcare professions and reporting on at least one learning outcome as classified by Kirkpatrick's evaluation framework were included in this review. Ten out of 633 abstracts reviewed met the study inclusion criteria. Several studies reported on more than one learning outcome. Two studies focused on learner reactions to interprofessional delirium education; three studies focused on learning outcomes (e.g. delirium knowledge); six studies focused on learner behavior in practice; and six studies reported on learning results (e.g. patient outcomes), mainly changes in delirium rates post-intervention. Studies reporting changes in patient outcomes following the delirium education intervention used an interprofessional practice (IPP) intervention in combination with interprofessional education (IPE). Our review of the limited evidence suggests that IPE programs may influence team and patient outcomes in delirium care. More systematic studies of the effectiveness of interprofessional delirium education interventions are needed.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".