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Record W2397265159

Health Policy: Kirby: “Outrageous” delay in health protection funding

2004· article· en· W2397265159 on OpenAlexvenueaboutno aff
Allison Gandey

Bibliographic record

VenueCanadian Medical Association Journal · 2004
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Agency (philosophy)AccountabilityCommitPublic administrationNothingLawPolitical scienceMedicineSociologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

Senator Michael Kirby is challenging the federal government to invest in health protection within the year. “Three studies in 10 years have recommended this, yet nothing has happened. Frankly, it's outrageous. The time has come to stop studying and start taking action,” Kirby said. Kirby's Standing Committee on Social Affairs, Science and Technology recently assessed the state of public health in Canada and evaluated a report by the national advisory group on SARS, chaired by Dr. David Naylor. Kirby's committee strongly supports Naylor's “comprehensive report — probably the first of its kind in Canada.” It also recommends a 12-month time- table for implementing the recommendations, including a new independent Canadian public health agency. “This is our way of forcing public accountability,” Kirby told CMAJ. A spokesperson for then-Health Minister Anne McLellan said that although she intends to move forward on this, she would not commit to a year. “There is a lot of work to be done and this will take time,” said Farah Mohamed. Naylor welcomes the Kirby report and hopes it will give Prime Minister Paul Martin “additional ammunition and ideas to move ahead rapidly.” Naylor called for $700 million per year in new federal spending by 2007. But Kirby said funding should, if possible, come from existing sources. Kirby also advised against requiring federal–provincial agreement before measures such as a new agency can move forward. “We didn't want this to get bogged down by federal–provincial interactions. ... [T]here are many actions the federal government can take alone, and we feel very strongly that such action is needed now.” — Allison Gandey, CMAJ

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)0.036
metaresearch head score (Gemma)0.087
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: Editorial · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score0.516

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.087
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.002
Science and technology studies0.0230.014
Scholarly communication0.0220.021
Open science0.0050.012
Research integrity0.0730.063
Insufficient payload (model declined to judge)0.0300.007

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 head0.046
GPT teacher head0.432
Teacher spread0.387 · 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
GenreEditorial

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
Published2004
Admission routes2
Has abstractyes

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