Differences in adult day services center characteristics by center ownership: United States, 2012.
Bibliographic record
Abstract
KEY FINDINGS: Data from the National Study of Long-Term Care Providers. In 2012, 40% of the 4,800 adult day services centers were for-profit entities, serving nearly one-half of the 272,300 center participants. About 60% of adult day services centers used a standardized tool to screen for cognitive impairment, and about 20% used a standardized tool for depression screening. A greater percentage of for-profit than nonprofit centers used these tools. More than one-half of adult day services centers provided skilled nursing, therapeutic, and social work services, while less than one-half of centers provided mental health, pharmacy, and dental services. With the exception of social work services, a greater percentage of for-profit than nonprofit centers provided these services. Almost all adult day services centers provided daily transportation to and from the center. The most recent data estimate that 4,800 adult day services centers nationwide serve nearly a quarter million participants daily (1). Unlike other long-term care providers, such as nursing homes, home health agencies, hospices, and residential care communities, the majority of adult day services centers are nonprofit (1). However, for-profit ownership of adult day services centers appears to be increasing, from 27% in 2010 to 40% in 2012 (2). Using data from the National Study of Long-Term Care Providers, this report presents national estimates for characteristics of adult day services centers in 2012 and compares them by type of center ownership.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".