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Record W3047321073 · doi:10.1377/hlthaff.2019.01554

Compared With Other Countries, Women In The US Are More Likely Than Men To Forgo Medicines Because Of Cost

2020· article· en· W3047321073 on OpenAlexaboutno aff
Jamie R. Daw, Michael R. Law

Bibliographic record

VenueHealth Affairs · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBusinessFamily medicineEnvironmental health

Abstract

fetched live from OpenAlex

Cost-related nonadherence to prescription medicines is a common problem with important implications for population health. Relative to men, women may be more vulnerable to cost-related nonadherence because of higher health needs and lower financial resources. Using data from the Commonwealth Fund International Health Policy Survey, we compared cost-related nonadherence among younger (ages 18-64) and older (ages 65 and older) women and men in eleven high-income countries. Among younger adults, the unadjusted female-male disparity was larger in the US compared with other countries: One in four younger women reported cost-related nonadherence compared with one in seven younger men. This large disparity persisted after adjustment for age, income, and chronic conditions. We also found smaller but significant female-male differences among younger women in Australia and Canada. We did not find significant female-male differences among older adults in adjusted analyses in any country. Higher rates of cost-related nonadherence among younger women, and US women in particular, may produce important sex-related disparities in health outcomes that should be further explored.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.074
GPT teacher head0.311
Teacher spread0.238 · 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 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

Citations5
Published2020
Admission routes1
Has abstractyes

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