A Comparative Analysis of Costs to Government for Home Care and Long-term Residential Care Services, Standardized for Client Care Needs
Bibliographic record
Abstract
This paper reports on the results of analyses using administrative data from British Columbia for 10 years from fiscal 1987/1988 to 1996/1997, inclusive, to examine the comparative costs to government of long-term home care and residential care services. The analyses used administrative data for hospital care, physician care, drugs, and home care and residential long-term care. Direct comparisons for cost and utilization data were possible, as the same care-level classification system is used in BC for home care and residential care clients. Given significant changes in the type and/or level of care of clients over time, a full-time equivalent client strategy was used to maximize the accuracy of comparisons. The findings suggest that, in general, home care can be a lower-cost alternative to residential care for clients with similar care needs. The difference in costs between home care and residential care services narrows considerably for those who change their type and/or level of care, and home care was found to be more costly than long-term institutional care for home care clients who died. The findings from this study indicate that with the appropriate substitution for residential care services, in a planned and targeted manner, home care services can be a lower-cost alternative to residential long-term care in integrated systems of care delivery that include both sets of services.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".