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Record W2287024294 · doi:10.14288/1.0048514

The Canadian Rx atlas, 3rd edition

2014· article· en· W2287024294 on OpenAlexaffabout
Steve Morgan, Kate Smolina, Dawn Mooney, Colette B. Raymond, Meryn Louisa Bowen, Chris Gorczynski, Kimberley A. Rutherford Basham

Bibliographic record

VenuecIRcle (University of British Columbia) · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Modeling and Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAtlas (anatomy)Geology

Abstract

fetched live from OpenAlex

"Canadians spent almost $23-billion on prescription drugs at retail pharmacies in 2012/13 – or over $650 per capita. That is a lot of money. However, after adjusting for general inflation, spending per capita actually fell over the past five years – despite the fact that the population was getting older. This 3rd edition of The Canadian Rx Atlas breaks down retail spending on prescription drugs Canada, providing a detailed portrait of the factors driving spending trends over time and variations across provinces. The Atlas gives a first-ever portrait of age- and sex-specific patterns of prescription drug use and costs across provinces. It also provides first-of-kind estimates of the source of financing for the prescriptions filled in every province. Unique to the Canadian Rx Atlas, these details are not provided simply for all spending on prescription drugs; it also provides these details for each of 33 clinically and economically important therapeutic categories." -CHSPR website

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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: Other · Consensus signal: none
Teacher disagreement score0.195
Threshold uncertainty score0.654

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.021
Science and technology studies0.0030.001
Scholarly communication0.0050.002
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1950.085

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 head0.007
GPT teacher head0.140
Teacher spread0.133 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations12
Published2014
Admission routes2
Has abstractyes

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