Effectiveness of an intensive care unit family education intervention on delirium knowledge: a pre-test post-test quasi-experimental study
Bibliographic record
Abstract
PURPOSE: To create, validate, and refine an intensive care unit (ICU) delirium education intervention to prepare family members to partner with the ICU care team to detect delirium symptoms and prevent and manage delirium using nonpharmacological strategies. METHODS: In this pre-test post-test quasi-experimental study, consecutive eligible family members of critically ill patients admitted to an ICU completed an ICU Family Education Delirium intervention in two parts: 1) six-minute video on ICU delirium (risk factors, prevention/management, symptoms, communication with the ICU care team), and 2) two case vignettes to practice detecting delirium using family-administered delirium detection questionnaires (Family Confusion Assessment Method [FAM-CAM] and Sour Seven). Family members' delirium knowledge was measured before, immediately after, and two weeks following the intervention using the Caregiver ICU Delirium Knowledge Questionnaire (CIDKQ). RESULTS: Of 99 family members recruited over eight months, 81 (82%) completed the intervention and 63 (63/81, 78%) completed all follow-up questionnaires. Family members' delirium knowledge improved significantly following the intervention (pre-CIDKQ, 14; 95% confidence interval [CI], 13 to 15; post-CIDKQ, 17; 95% CI, 16 to 17; P < 0.001) and was retained two weeks after the intervention (CIDKQ 16; 95% CI, 16 to 17; P < 0.001). This included increased knowledge regarding delirium risk factors (e.g., medication, mechanical ventilation), prevention/management (e.g., orientation, day/night routine), and symptoms of delirium. More family members correctly detected delirium symptoms in case vignettes using the Sour Seven (92%) compared with the FAM-CAM (78%). CONCLUSIONS: A video-based ICU delirium education intervention is effective in educating family members about prevention, detection, and management of delirium.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".