Translation, Adaptation, and Criterion Validation of the Family Caregiver Assessment Tool for French-Speaking Cardiovascular Patients in Canada (FAM-CAM-Fr)
Bibliographic record
Abstract
BackgroundDelirium is a common yet underdiagnosed condition in hospitalized older adults, particularly challenging to detect early in cardiology settings. Although delirium assessment tools improve detection rates, observations by family caregivers of patients' cognitive changes can offer valuable insights, supplementing assessments by healthcare professionals. However, validated French-language tools for family caregivers to assess delirium in acute care settings in Canada are lacking.PurposeTranslate, culturally adapt, and validate the Family Confusion Assessment Method for French-speaking cardiovascular patients and their caregivers (FAM-CAM-Fr).MethodsThe translation and cultural adaptation of the FAM-CAM were conducted following the guidelines of Sousa and Rojjanasrirat (2011). Criterion validation involved 100 dyads of family caregivers and hospitalized cardiovascular patients. The FAM-CAM-Fr's performance was assessed by comparing it to the Confusion Assessment Method (CAM) and the DSM-5 diagnostic criteria for delirium. Measures of sensitivity, specificity, and agreement with the CAM were calculated.ResultsThe FAM-CAM-Fr showed high specificity (92.6%) but low sensitivity (58%) in detecting delirium. Cohen's Kappa indicated a moderate agreement (>0.50) between the FAM-CAM-Fr and the CAM. Despite family caregivers using the tool without prior training, indicating its usability in real-world settings, sensitivity was lower compared to studies that included caregiver training, though specificity was similar.ConclusionThe FAM-CAM-Fr is promising as a specific tool for screening delirium in cardiovascular patients. Despite its low sensitivity, its high specificity indicates that it is effective at ruling out delirium. Future research should focus on further validation across various settings.
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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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".