MétaCan
Menu
Back to cohort
Record W2168415188 · doi:10.1377/hlthaff.w5.152

Prescription Drug Coverage And Seniors: Findings From A 2003 National Survey

2005· article· en· W2168415188 on OpenAlexaboutno aff
Dana Gelb Safran, Patricia Neuman, Cathy Schoen, Michelle Kitchman, Ira B. Wilson, Barbara Cooper, Angela Li, Hong Chang, William H. Rogers

Bibliographic record

VenueHealth Affairs · 2005
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsPrescription drugMedical prescriptionQuarter (Canadian coin)MedicaidPharmacyMedicineFamily medicineMedicare Part DEnvironmental healthGerontologyHealth careNursingEconomic growth

Abstract

fetched live from OpenAlex

Beginning in 2006 the Medicare Prescription Drug, Improvement, and Modernization Act (MMA) will offer pharmacy benefits to forty-two million Medicare beneficiaries nationwide. In a 2003 national survey of Medicare beneficiaries age sixty-five and older, more than one-quarter reported no prescription coverage, and nearly half of low-income seniors in some states lacked coverage. Wide coverage differences among states highlight implementation challenges and the need for tailored enrollment strategies. Evidence of Medicaid's highly effective coverage delineates the importance of assuring this group's continued protection under Part D plans. Reports of complex drug regimens, multiple prescribing physicians and pharmacies, nonadherence, and reimportation demonstrate the challenges of integrating seniors' prescription care. We discuss MMA's potential to improve quality and the need to monitor performance.

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 score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.100
GPT teacher head0.400
Teacher spread0.299 · 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

Citations233
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueHealth AffairsSame topicPharmaceutical Practices and Patient OutcomesFrench-language works237,207