Psychometric evaluation of the family caregiver ICU delirium knowledge questionnaire
Bibliographic record
Abstract
BACKGROUND: Delirium is a common condition in critically ill patients, affecting nearly half of all patients admitted to an intensive care unit (ICU). Family caregivers of critically ill patients can be partners in the early recognition, prevention and management of delirium provided they are aware of the signs/symptoms and appropriate non-pharmacological strategies that might be taken. Valid, reliable instruments that assess family caregiver knowledge are essential so that nurses can prepare family caregivers to be effective partners. The purpose of the current study was to (a) adapt an existing caregiver delirium knowledge questionnaire (CDKQ) for use by nurses to measure a family caregiver's delirium knowledge in the ICU; and (b) examine the psychometric properties and structure of the adapted Caregiver ICU Delirium Knowledge Questionnaire (CIDKQ). METHODS: In this cross-sectional study, a multidisciplinary team developed the 21-item CIDKQ (possible score range: 0-21) and administered it to 158 family caregivers of critically ill patients. Descriptive statistics were examined for all variables. The CIDKQ was analyzed for face validity, content validity, reliability and internal consistency. RESULTS: The mean CIDKQ score was 14.1 (SD: 3.5, range = 2 to 21). Path analysis revealed that a family caregiver's delirium knowledge in the actions and symptoms dimensions had a direct effect on knowledge of delirium risk factors. The CIDKQ was found to have face validity and reliability (Cronbach's α = 0.79). CONCLUSIONS: The findings indicated good validity and reliability of the CIDKQ as a measure of ICU delirium knowledge in family caregivers of critically ill patients.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".