Racial/ethnic minority and low-income hotspots and their geographic proximity to integrated care providers
Bibliographic record
Abstract
BACKGROUND: The high prevalence of mental health issues among clients attending substance abuse treatment (SAT) has pressured treatment providers to develop integrated substance abuse and mental health care. However, access to integrated care is limited to certain communities. Racial and ethnic minority and low-income communities may not have access to needed integrated care in large urban areas. Because the main principle of health care reform is to expand health insurance to low-income individuals to improve access to care and reduce health disparities among minorities, it is necessary to understand the extent to which integrated care is geographically accessible in minority and low-income communities. METHODS: National Survey of Substance Abuse Treatment Services data from 2010 were used to examine geographic availability of facilities offering integration of mental health services in SAT programs in Los Angeles County, California. Using geographic information systems (GIS), service areas were constructed for each facility (N = 402 facilities; 104 offering integrated services) representing the surrounding area within a 10-minute drive. Spatial autocorrelation analyses were used to derive hot spots (or clusters) of census tracts with high concentrations of African American, Asian, Latino, and low-income households. Access to integrated care was reflected by the hot spot coverage of each facility, i.e., the proportion of its service area that overlapped with each type of hot spot. RESULTS: GIS analysis suggested that ethnic and low-income communities have limited access to facilities offering integrated care; only one fourth of SAT providers offered integrated care. Regression analysis showed facilities whose service areas overlapped more with Latino hot spots were less likely to offer integrated care, as well as a potential interaction effect between Latino and high-poverty hot spots. CONCLUSION: Despite significant pressure to enhance access to integrated services, ethnic and racial minority communities are disadvantaged in terms of proximity to this type of care. These findings can inform health care policy to increase geographic access to integrated care for the increasing number of clients with public health insurance.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".