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Record W2140830921 · doi:10.1377/hlthaff.2011.1301

Survey Results Show That Adults Are Willing To Pay Higher Insurance Premiums For Generous Coverage Of Specialty Drugs

2012· article· en· W2140830921 on OpenAlexaff
John A. Romley, Yuri Sanchez, John R. Penrod, Dana P. Goldman

Bibliographic record

VenueHealth Affairs · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsInstitute of Health Economics
Fundersnot available
KeywordsLiberian dollarSpecialtyActuarial scienceCost sharingWillingness to payBusinessValue (mathematics)Health insuranceMedicare Part DMedicineFamily medicineFinanceHealth carePrescription drugEconomicsMedical prescriptionEconomic growthPharmacology

Abstract

fetched live from OpenAlex

Generous coverage of specialty drugs for cancer and other diseases may be valuable not only for sick patients currently using these drugs, but also for healthy people who recognize the potential need for them in the future. This study estimated how healthy people value insurance coverage of specialty drugs, defined as high-cost drugs that treat cancer and other serious health conditions like multiple sclerosis, by quantifying willingness to pay via a survey. US adults were estimated to be willing to pay an extra $12.94 on average in insurance premiums per month for generous specialty-drug coverage--in effect, $2.58 for every dollar in out-of-pocket costs that they would expect to pay with a less generous insurance plan. Given the value that people assign to generous coverage of specialty drugs, having high cost sharing on these drugs seemingly runs contrary to what people value in their health insurance.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.301
GPT teacher head0.404
Teacher spread0.103 · 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

Citations24
Published2012
Admission routes1
Has abstractyes

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