Development and Validation of Caregiver Risk Evaluation (CaRE): A New Algorithm to Screen for Caregiver Burden
Bibliographic record
Abstract
Objective: The main objective was to develop a decision-support tool to assess the risk of caregiver burden, the Caregiver Risk Evaluation (CaRE) algorithm. Methods: Home care clients were assessed using the Resident Assessment Instrument for Home Care (RAI-HC). Their caregiver completed the 12-item Zarit Burden Interview (ZBI), the main dependent measure, which was linked to the RAI-HC. Results: In the sample ( n = 344), 48% were aged 85+ years and 61.6% were female. The algorithm can be collapsed into four categories (low, moderate, high, and very high risk). Relative to the low-risk group, clients in the very high-risk group had an odds ratio of 5.16 (95% confidence interval: [2.05, 12.9]) for long-term care admission, after adjusting for client age, sex, and regional health authority. Discussion: The CaRE algorithm represents a new tool to be used by home care clinicians as they proactively plan for the needs of clients and their caregivers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".