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Record W2483488604 · doi:10.1377/hlthaff.2015.1612

Racial Disparities In Geographic Access To Primary Care In Philadelphia

2016· article· en· W2483488604 on OpenAlexaff
Elizabeth Brown, Daniel Polsky, Corentin Barbu, J Seymour, David Grande

Bibliographic record

VenueHealth Affairs · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsInstitute of Health Economics
Fundersnot available
KeywordsCensusCensus tractPrimary careOddsGeographyHealth careAmerican Community SurveyHealth equityMedicineDemographySocioeconomic statusGerontologyEnvironmental healthSocioeconomicsPublic healthPopulationLogistic regressionFamily medicineNursingEconomic growthSociology

Abstract

fetched live from OpenAlex

Primary care is often thought of as the gateway to improved health outcomes and can lead to more efficient use of health care resources. Because of primary care's cardinal importance, adequate access is an important health policy priority. In densely populated urban areas, spatial access to primary care providers across neighborhoods is poorly understood. We examined spatial variation in primary care access in Philadelphia, Pennsylvania. We calculated ratios of adults per primary care provider for each census tract and included buffer zones based on prespecified drive times around each tract. We found that the average ratio was 1,073; the supply of primary care providers varied widely across census tracts, ranging from 105 to 10,321. We identified six areas of Philadelphia that have much lower spatial accessibility to primary care relative to the rest of the city. After adjustment for sociodemographic and insurance characteristics, the odds of being in a low-access area were twenty-eight times greater for census tracts with a high proportion of African Americans than in tracts with a low proportion of African Americans.

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)0.001
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: none
Teacher disagreement score0.641
Threshold uncertainty score0.940

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.032
GPT teacher head0.366
Teacher spread0.334 · 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

Citations138
Published2016
Admission routes1
Has abstractyes

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