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Record W1933402072 · doi:10.1377/hlthaff.2014.1258

Most Uninsured Adults Could Schedule Primary Care Appointments Before The ACA, But Average Price Was $160

2015· article· en· W1933402072 on OpenAlexaff
Brendan Saloner, Daniel Polsky, Genevieve M. Kenney, Katherine Hempstead, Karin V. Rhodes

Bibliographic record

VenueHealth Affairs · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsInstitute of Health Economics
Fundersnot available
KeywordsMedicaidHealth insurancePrimary carePoverty levelPatient Protection and Affordable Care ActPovertyScheduleTelephone surveyBusinessFamily medicineHealth reformHealth careMedicineEconomic growthEconomicsMarketing

Abstract

fetched live from OpenAlex

Provisions of the Affordable Care Act (ACA) allow millions more Americans to obtain health insurance. However, a sizable number of people remain uninsured because they live in states that have not expanded Medicaid coverage or because they feel that Marketplace coverage is not affordable. Using data from a ten-state telephone survey in which callers posed as patients, we examined prices for primary care visits offered by physician offices to new uninsured patients in 2012-13, prior to ACA insurance expansions. Patients were quoted a mean price of $160. Significantly lower prices for the uninsured were offered by family practice offices compared to general internists, in offices participating in Medicaid managed care plans, and in federally qualified health centers. Prices were also lower for offices in ZIP codes with higher poverty rates. Only 18 percent of uninsured callers were told that they could bring less than the full amount to the visit and arrange to pay the rest later. ACA insurance expansions could greatly decrease out-of-pocket spending for low-income adults seeking primary care. However, benefits of health reform are likely to be greater in states expanding Medicaid eligibility.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.862
Threshold uncertainty score0.902

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.044
GPT teacher head0.269
Teacher spread0.225 · 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 designNot applicable
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

Citations19
Published2015
Admission routes1
Has abstractyes

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