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Record W4232472393 · doi:10.3138/cpp.37.4.563

Socioeconomic Status and the Use of Medicines in the Ontario Public Drug Program

2011· article· en· W4232472393 on OpenAlexaffvenueabout
Sara Allin, Audrey Laporte

Bibliographic record

VenueCanadian Public Policy · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSocioeconomic statusMedical prescriptionMedicinePrescription drugEnvironmental healthPublic healthPharmacyDrugGerontologyFamily medicinePopulationNursing

Abstract

fetched live from OpenAlex

Residents of Ontario aged 65 years and older are covered by a provincially funded prescription drug program. The aim of this paper is to assess the extent of inequity in prescription drug use for people eligible for Ontario Drug Benefit coverage, and to explore the different possible explanations for inequities. The analyses draw on the Canadian Community Health Survey from 2005, which is linked to pharmacy and Ontario Health Insurance Plan claims data. We model the number of therapeutically different prescription drugs and the total expenditures on medications on a set of health, demographic, and socioeconomic indicators, and we calculate the concentration index of income-related inequality in medicine use. The results show that low-income individuals who have enrolled in the reduced cost-sharing program on average use more medications than those with higher income, even after adjusting for a comprehensive set of health and demographic variables. While Ontario's public drug program appears to have ensured access to medications for low-income seniors, the results of this study raise concerns about the potential inappropriateness of medication use and point to a gap in drug policy in Ontario.

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)0.010
metaresearch head score (Gemma)0.004
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 score0.599
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.491
GPT teacher head0.386
Teacher spread0.104 · 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

Citations24
Published2011
Admission routes3
Has abstractyes

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