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Record W7094271776

The financial burden of prescription drugs for neurological conditions in Canada: Results from the National Population Health Study of Neurological Conditions

2017· other· en· W7094271776 on OpenAlexaboutno aff

Bibliographic record

VenueRePEc: Research Papers in Economics · 2017
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Electron Microscopy Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMedical prescriptionVulnerability (computing)Qualitative researchCorporate governancePrescription drugPopulationPrimary care
DOInot available

Abstract

fetched live from OpenAlex

This paper describes the current situation in Canada concerning the availability and use of prescription drugs for neurological conditions. We conducted semi-structured qualitative interviews with health care providers, administrators, community organization representatives, opinion leaders and policy makers. The analysis revealed three primary themes related to the availability of and access to prescription drugs to treat neurological conditions. First, we learned that across Canada there is significant vulnerability and a need for advocacy on behalf of people living with these conditions. Second, we learned that the heightened level of vulnerability and need for advocacy stems in part from the significant differences in the drug coverage available in the different provinces and territories. As a result, there are significant inequities across Canada. Third, we determined that the existing situation is also due to the current approach to health governance (i.e., accountability, transparency). Our study provides evidence for the urgent need for a formal discourse on national pharmacare in Canada, with representatives of neurological conditions having a voice at the table.

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)0.000
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.022
GPT teacher head0.361
Teacher spread0.339 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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