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Record W4387729577 · doi:10.1377/hlthaff.2023.00996

Health Benefits In 2023: Premiums Increase With Inflation And Employer Coverage In The Wake Of <i>Dobbs</i>

2023· article· en· W4387729577 on OpenAlexaboutno aff
Gary Claxton, Matthew Rae, Anthony V. D’Amico, Emma Wager, Aubrey Winger, Michelle T. Long

Bibliographic record

VenueHealth Affairs · 2023
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Supreme courtInflation (cosmology)BusinessAbortionHealth careActuarial scienceDemographic economicsEconomicsLawPolitical scienceEconomic growth

Abstract

fetched live from OpenAlex

In 2023 the average annual premium for employer-sponsored family health insurance coverage was $23,968—an increase of $1,505 (7 percent) from 2022. Both single and family premiums increased faster in 2023 than in 2022, in a period of generally high inflation throughout the US economy. On average, covered workers contributed 17 percent ($1,401) of the cost of single coverage and 29 percent ($6,575) of the cost of family coverage. When compared to employers’ perceptions of the number of primary care providers in their networks, a smaller share of employers believed that their provider networks had a sufficient number of mental health and substance abuse providers to provide timely access to services. One-quarter of employers indicated that their employees had a “high” level of concern with the level of cost sharing required by their plans. When asked about abortion coverage in the wake of the Supreme Court Dobbs decision, almost a third of large employers reported that their largest plan covered abortion in most or all circumstances.

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)0.002
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: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.020
GPT teacher head0.298
Teacher spread0.278 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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