Predicting in‐home time of community care professionals
Bibliographic record
Abstract
The aim of this study was to test whether the client homebound score (CHS), the case management intensity score (CMIS) and the client priority visit score (CPVS) could be used to predict in-home time of professional caregivers in the Aspen community care program. A random sample of 34 community care clients from the different geographical areas of the Aspen Regional Health Authority was selected and the home visits for each client were tracked for three months. Information such as client demographics, the client diagnostic category, number and in-home time of visits was collected. In addition, the CHS, the CMIS and the CPVS were measured for each client. Data were analyzed, using a robust variance estimator regression model. CMIS was found to be the best predictor of in-home time (coefficient 9.521, p > 0.001), followed by the CHS and the CPVS.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".