Development and validation of a screener based on interRAI assessments to measure informal caregiver wellbeing in the community
Bibliographic record
Abstract
BACKGROUND: Informal caregivers are invaluable partners of the health care system. However, their caring responsibilities often affect their psychological wellbeing and ability to continue in their role. It is of paramount importance to easily identify caregivers that would benefit from immediate assistance. METHODS: In this nonexperimental cohort study, a cross-sectional analysis was conducted among 362 informal caregivers (mean age 64.1 years, SD ± 13.1) caring for persons with high care needs (mean age 78.6 years, SD ± 15.0). Caregivers were interviewed using an interRAI-based self-reported survey with 82 items covering characteristics of caregivers including key aspects of wellbeing. A factor analysis identified items in the caregiver survey dealing with subjective wellbeing that were compared against other wellbeing measures. A screener, called Caregiver Wellbeing Index (CWBI), consisting of four items with response scores ranging from 0 to 2 was created. The CWBI was validated in a follow-up study in which 1020 screeners were completed by informal caregivers of home care clients. Clinical assessments of the care recipients (n = 262) and information on long-term care home (LTCH) admission (n = 176) were linked to the screener dataset. The association between the CWBI scores and caregiver and care recipient characteristics were assessed using logistic regression models and chi-square tests. The reliability of CWBI was also measured. RESULTS: The CWBI scores ranging from zero to eight were split in four 'wellbeing' levels (excellent, good, fair, poor). In the validation study, fair/poor psychological wellbeing was strongly associated with caregiver reports of inability to continue in their role; conflict with family; or feelings of distress, anger, or depression (P < 0.0001). Caregivers caring for a care recipient that presented changes in behavior, cognition, and mood were more likely to present fair/poor wellbeing (P < 0.0001). Additionally, caregivers with high CWBI scores (poor wellbeing) were also more likely to provide care for someone who was admitted to a LTCH (OR 3.52, CI 1.32-9.34) after controlling for care recipient and caregiver characteristics. The Cronbach alpha value 0.89 indicated high reliability. CONCLUSION: The CWBI is a valid screener that can easily identify caregivers that might benefit from further assessment and interventions.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".