Mom and Pop Versus the Big Boys: Adult Family Homes as Providers of Medicaid-Funded Residential Care
Bibliographic record
Abstract
This paper compares assisted living apartments (ALs), adult residential care facilities (ARCs), and small adult family homes (AFHs) for Medicaid residents in Washington State, with particular emphasis on the settings, staffing, services, and policies of AFHs. We targeted for enrollment all residents entering an AFH, ARC, or AL setting on Medicaid/state funding in a three-county area of Washington State. We obtained information on 199 settings, interviewing administrative and direct care providers. AFHs are smaller than ARCs and ALs and less likely to be part of a chain, with no significant difference in staffing ratios of registered nurses and licensed practical nurses. Sixty-four percent of AFH residents were receiving public funds compared to 32% of AL residents. AFHs report admitting residents with more activities of daily living needs, health conditions, and behavior problems. They are less likely to have autonomy-related policies, and they provide more services and fewer activities. While attention should continue to be paid to staff supports, policy and practice should support the continued role of AFHs, which are of special interest because of their potential to provide more homelike, less costly care but with possible trade-offs compared to larger facilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".