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Record W2487725774 · doi:10.18192/riss-ijhs.v2i2.1517

Quelle est la Part des Regimes Publics d’Assurance- Medicament aux Resultats de Sante? Une Etude de Cas Multiples au Nouveau-Brunswick, en Ontario, et au Quebec

2012· article· en· W2487725774 on OpenAlexaffvenueabout
Marika Alary-Vanasse, Sanni Yaya

Bibliographic record

VenueRevue interdisciplinaire des sciences de la santé - Interdisciplinary Journal of Health Sciences · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsIntertek (Canada)University of Ottawa
Fundersnot available
KeywordsMedical prescriptionResidencePopulationPolitical sciencePrescription drugHealth careGerontologyMedicineDemographyEnvironmental healthSociologyNursing

Abstract

fetched live from OpenAlex

Canadian provincial and territorial drug plans vary considerably in terms of eligibility criteria, and most of the insurance plans provided are subject to caps, cost sharing and exclusions. In fact, Canadians have unequal access to prescription medications, depending on their socio-economic status and place of residence. This comes at time then the effects of population aging, an increasing number of people with multiple chronic illnesses and innovations in the prescription medications field have brought about an increase in spending on these products. The question raised by this study is does a province with a better prescription drug plan have a healthier population? This study compared coverage of prescription drug plans among people 65 years old and over in three provinces: New Brunswick, Ontario and Quebec. The results of our study suggest that senior citizen health does not seem to improve. Therefore, it is impera- tive that additional research be conducted to determine the true impact of prescription drug plans on health outcomes. This would allow for the creation of tailor-made, better-targeted programs and policies to meet community needs more adequately.

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)0.121
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1210.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0040.007
Scholarly communication0.0010.004
Open science0.0020.001
Research integrity0.0000.001
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 head0.205
GPT teacher head0.479
Teacher spread0.274 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2012
Admission routes3
Has abstractyes

Explore more

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