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Record W2615847213

Primary Care Appointment Availability and the ACA Insurance Expansions.

2017· article· en· W2615847213 on OpenAlexaff
Molly Candon, Daniel Polsky, Brendan Saloner, Douglas Wissoker, Katherine Hempstead, Genevieve M. Kenney, Karin V. Rhodes

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsPopulation Health Research Institute
Fundersnot available
KeywordsMedicaidPrimary careHealth insuranceAuditPatient Protection and Affordable Care ActFamily medicineMedicinePrivate insuranceBusinessHealth carePolitical scienceAccountingLaw
DOInot available

Abstract

fetched live from OpenAlex

In the current debate in Congress over the Affordable Care Act (ACA), the issue of provider access is a major concern. Fortunately, our 10-state audit study published in JAMA Internal Medicine finds that primary care appointment availability for new patients with Medicaid increased 5.4 percentage points between 2012 and 2016 and remained stable for patients with private coverage. Over the same period, both Medicaid patients and the privately insured experienced a one-day increase in median wait times. Higher appointment availability for Medicaid patients is a surprising result given the increase in demand for care from millions of new Medicaid enrollees. In this Issue Brief, we summarize our study’s findings, expand on possible explanations, and extend the analysis by examining the relationship between appointment availability and state-level Medicaid expansions. We find that access to primary care increased for Medicaid patients only in states that extended Medicaid eligibility to low-income, nonelderly adults. Combined, these results suggest coverage provisions in the ACA have not overwhelmed primary care capacity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.056
GPT teacher head0.236
Teacher spread0.180 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2017
Admission routes1
Has abstractyes

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