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Record W911318732 · doi:10.1177/009885880803400404

Right Idea, Wrong Result—Canada's Access to Medicines Regime

2008· article· en· W911318732 on OpenAlexaboutno aff
Paige E. Goodwin

Bibliographic record

VenueAmerican Journal of Law & Medicine · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCITESDeveloping countryPopulationEssential medicinesDeveloped countryHealth careEnvironmental healthEconomic growthEconomics

Abstract

fetched live from OpenAlex

In 2007, an estimated 33.2 million people were living with HIV, 2.5 million had become infected, and 2.1 million died from the virus. The majority of infected individuals reside in Africa, where in some countries as many as 33.4% of adults have HIV. In developed countries, effective drug therapies have reduced AIDS-related deaths by over seventy percent each year. These drugs have been so effective that over the last two years the global number of individuals dying from AIDS-related illness has actually declined. These therapies, however, are currently sold for $10,000 USD a year, a purchase price that is not feasible for low income countries where the annual health expenditure may be only $29 per person. A lack of essential medicine is not only a problem for those suffering from AIDS. Low and middle-income countries are disproportionately burdened by many additional chronic and infectious illnesses. The World Health Organization (“WHO”) estimates that one third of the world's population cannot regularly access essential medicines. The WHO cites the high cost of drugs as one of the major hurdles countries face in obtaining access to medication. However, the high cost of these brand-name medications does not reflect their minimal production costs. Drug manufacturers can produce generic versions of these drugs for as little as 1/30 th of the cost of their brand-name counterparts.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.919

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 studies0.0000.001
Scholarly communication0.0000.000
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 head0.062
GPT teacher head0.321
Teacher spread0.259 · 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

Citations39
Published2008
Admission routes1
Has abstractyes

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