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Record W1969841974 · doi:10.1377/hlthaff.2013.0934

The Health Reform Monitoring Survey: Addressing Data Gaps To Provide Timely Insights Into The Affordable Care Act

2013· article· en· W1969841974 on OpenAlexaboutno aff
Sharon K. Long, Genevieve M. Kenney, Stephen Zuckerman, Dana E. Goin, Douglas Wissoker, Fredric Blavin, Linda J. Blumberg, Lisa Clemans-Cope, John Holahan, Katherine Hempstead

Bibliographic record

VenueHealth Affairs · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careQuarter (Canadian coin)BusinessHealth insuranceGovernment (linguistics)Patient Protection and Affordable Care ActSurvey data collectionHealth policyActuarial scienceEnvironmental healthMedicineEconomic growthEconomics

Abstract

fetched live from OpenAlex

The Health Reform Monitoring Survey (HRMS) was launched in 2013 as a mechanism to obtain timely information on the Affordable Care Act (ACA) during the period before federal government survey data for 2013 and 2014 will be available. Based on a nationally representative, probability-based Internet panel, the HRMS provides quarterly data for approximately 7,400 nonelderly adults and 2,400 children on insurance coverage, access to health care, and health care affordability, along with special topics of relevance to current policy and program issues in each quarter. For example, HRMS data from summer 2013 show that more than 60 percent of those targeted by the health insurance exchanges struggle with understanding key health insurance concepts. This raises concerns about some people's ability to evaluate trade-offs when choosing health insurance plans. Assisting people as they attempt to enroll in health coverage will require targeted education efforts and staff to support those with low health insurance literacy.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.912
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.150
GPT teacher head0.347
Teacher spread0.197 · 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.

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

Citations99
Published2013
Admission routes1
Has abstractyes

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