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Record W206446438 · doi:10.1177/003335490912400510

Improvements in Access to Care for Vulnerable Children in California between 2001 and 2005

2009· article· en· W206446438 on OpenAlexfundno aff
Gregory D. Stevens, Michael Seid, Kai‐Ya Tsai, Carmen N. West-Wright, Michael R. Cousineau

Bibliographic record

VenuePublic Health Reports · 2009
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsnot available
FundersPartenariat Canadien Contre Le CancerUniversity of Southern CaliforniaUniversity of California
KeywordsMedicineConfidence intervalOdds ratioPopulationDemographyPovertyOddsEnvironmental healthGerontologyFamily medicineLogistic regression

Abstract

fetched live from OpenAlex

OBJECTIVE: We examined population changes in access to care for children in California during a period of major efforts to improve access to care for children. METHODS: We used cross-sectional data on 36,010 children aged 0-19 years from the 2001 and 2005 California Health Interview Survey to assess population changes in access to care. We assessed changes in access by individual risk factors and a composite risk profile. RESULTS: In 2005, a smaller proportion of children were uninsured (8.2% vs. 10.9% in 2001), living in poverty (20.7% vs. 23.2% in 2001), and in families without a high school education (20.8% vs. 23.6% in 2001), all p<0.001. Before and after adjusting for these changes in risk, children were more likely in 2005 to have had a physician visit (odds ratio [OR] = 1.09, 95% confidence interval [CI] 1.07, 1.12) and dental visit (OR=1.11, 95% CI 1.08, 1.14). Children were slightly less likely in 2005 to have a regular source of care (OR=0.94, CI 0.91, 0.96). Children who had the highest risk profiles (> or = 4 risk factors) experienced the largest gains in access. For example, children with three and > or = 4 risk factors had gains in dental visits of 11 and 20 percentage points, respectively (p<0.001 for each), compared with < or = 3 percentage points for children with fewer risk factors. CONCLUSIONS: This study found improvements in physician and dental visits between 2001 and 2005 that were not fully explained by changes in insurance coverage or other demographic risk factors. Vulnerable children fared well during this period, suggesting that California may be making important and potentially replicable strides in reducing disparities.

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.043
GPT teacher head0.382
Teacher spread0.339 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2009
Admission routes1
Has abstractyes

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