MétaCan
Menu
Back to cohort
Record W2753595787 · doi:10.1377/hlthaff.2016.1582

Most Marketplace Plans Included At Least 25 Percent Of Local-Area Physicians, But Enrollment Disparities Remained

2017· article· en· W2753595787 on OpenAlexaboutno aff
Aditi P. Sen, Lena M. Chen, Donald F. Cox, Arnold M. Epstein

Bibliographic record

VenueHealth Affairs · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)BusinessHealth careHealth insuranceQuality (philosophy)Patient Protection and Affordable Care ActActuarial scienceEconomic growthEconomicsGeography

Abstract

fetched live from OpenAlex

The Affordable Care Act allows commercial insurers participating in the Marketplaces to vary the size of their provider networks as long as the providers are "sufficient" in numbers and types. Concerns have been growing over the increasing use of restricted-provider or narrow networks in Marketplace plans because of their implications for reduced access to care, but little is known about the breadth and stability of these networks over time or what types of enrollees choose such plans. Using national data, we found that in 2016, 60 percent of provider networks in plans offered in the federally facilitated Marketplaces included at least one-quarter of local-area physicians, and that consumers' access to broad-network plans remained stable between 2015 and 2016. Hispanic and low-income people made up a disproportionate share of enrollees in smaller-network plans (those with fewer than one-quarter of local-area physicians). It will be important to monitor the impact of narrow networks on access to and quality of care as well as on health outcomes.

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)0.004
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 score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0200.002

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 head0.045
GPT teacher head0.275
Teacher spread0.230 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueHealth AffairsSame topicHealthcare Policy and ManagementFrench-language works237,207