Association of Mandated Language Access Programming and Quality of Care Provided by Mental Health Agencies
Bibliographic record
Abstract
OBJECTIVE: This study examined the association between language access programming and quality of psychiatric care received by persons with limited English proficiency (LEP). METHODS: In 1999, the California Department of Mental Health required county Medicaid agencies to implement a "threshold language access policy" to meet the state's Title VI obligations. This policy required Medi-Cal agencies to provide language access programming, including access to interpreters and translated written material, to speakers of languages other than English if the language was spoken by at least 3,000, or 5%, of the county's Medicaid population. Using a longitudinal study design with a nonequivalent control group, this study examined the quality of care provided to Spanish speakers with LEP and a severe mental illness before and after implementation of mandatory language access programming. Quality was measured by receipt of at least two follow-up medication visits within 90 days or three visits within 180 days of an initial medication visit over a period of 38 quarter-years. RESULTS: On average, only 40% of Spanish-speaking clients received at least three medication follow-up visits within 180 days. In multivariate analyses, language access programming was not associated with receipt of at least two medication follow-up visits within 90 days or at least three visits within 180 days. CONCLUSIONS: This study found no evidence that language access programming led to increased rates of follow-up medication visits for clients with LEP.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".