Residential care in California: Spatial and temporal trends in facility development and care capacity
Bibliographic record
Abstract
The development of residential care has not kept pace with the growth of the older population in many places. We merged the California Department of Social Services residential care for the elderly dataset with census place data to document the growth of facilities and beds per older adults in all of California and in its three largest cities. From 1996 to 2015, residential care steadily increased in California by the number of facilities and beds relative to older adults. However, due to a consistently increasing older adult population, the Cities of San Diego and San Jose experienced gradual and intermittent decline in capacity per older adults, respectively, even as they added many beds to their inventories from the sporadic development of large assisted living and continuing care retirement communities. Additionally, San Jose and Los Angeles exhibited the most overlap in densities of facility development and oldest old adults, with San Diego showing less intersection in cartographic analyses. Understanding facility development and care capacity trends can help local agencies and jurisdictions in the United States and other countries discern whether planning policies and other geographical and development factors appropriately encourage the development of residential care and other long‐term care facilities .
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".