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Record W4307562380 · doi:10.1111/ssqu.13223

Education and polypharmacy: A national study of racial and ethnic variations

2022· article· en· W4307562380 on OpenAlexaff
Terrence D. Hill, Jason A. Ford, Harvey L. Nicholson

Bibliographic record

VenueSocial Science Quarterly · 2022
Typearticle
Languageen
FieldMedicine
TopicMedical Education and Admissions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPolypharmacyEthnic groupSocioeconomic statusRace and healthGerontologyHealth equityDemographyNational Health and Nutrition Examination SurveySocial classRace (biology)PsychologyMedicineSociologyHealth carePopulationPolitical scienceEconomicsEconomic growthGender studies

Abstract

fetched live from OpenAlex

Abstract Objective We examine the association between college education and the number of medications used/misused in the past year. We also consider the possibility of differential socioeconomic returns to health for racial/ethnic minorities. Methods The data come from the 2015–2019 National Survey on Drug Use and Health ( n = 144,589). Results In accordance with human capital theory, we found that, in the full sample and white subsample, college education was associated with lower levels of polypharmacy, even with adjustments for financial insecurity, health, and lifestyle. Consistent with the diminished return theory, we observed that college education was mostly unrelated to polypharmacy among black and Hispanic individuals. While health commodity theory was supported among Asians, health disparity theory was confirmed among individuals of other races and ethnicities. Conclusion The most important implication of our study is that polypharmacy can be simultaneously structured by durable systems of social stratification, including education, race, and ethnicity.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.041
GPT teacher head0.433
Teacher spread0.392 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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