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Record W2145939522 · doi:10.1186/1747-597x-8-34

Racial/ethnic minority and low-income hotspots and their geographic proximity to integrated care providers

2013· article· en· W2145939522 on OpenAlex
Erick G. Guerrero, Dennis Kao

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSubstance Abuse Treatment Prevention and Policy · 2013
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of SaskatchewanRegina Qu'Appelle Health Region
FundersNational Institute on Drug AbuseSchool of Social Work, University of Southern CaliforniaUniversity of Southern California
KeywordsEthnic groupMental healthService providerHealth careGeographic information systemIntegrated careSubstance abuseBusinessGeographyEnvironmental healthService (business)MedicineSocioeconomicsEconomic growthPolitical scienceMarketingSociologyCartographyPsychiatryEconomics

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.294
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it