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Record W2893940867 · doi:10.1503/cmaj.171045

Improving the quality of health care in Canada

2018· article· en· W2893940867 on OpenAlexaffvenueabout
Irfan A. Dhalla, Joshua Tepper

Bibliographic record

VenueCanadian Medical Association Journal · 2018
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsToronto East General HospitalPublic Health OntarioUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsHealth careQuality (philosophy)Health care qualityKey (lock)MedicineComputer scienceData sciencePolitical scienceComputer security

Abstract

fetched live from OpenAlex

KEY POINTS Canadians are healthier than ever before,[1][1] and in many respects, the quality of health care provided to Canadians is also better than ever.[2][2]–[4][3] However, there are still large gaps between what we know to be high-quality health care and the care that many Canadians receive

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)0.005
metaresearch head score (Gemma)0.024
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: Empirical · Consensus signal: none
Teacher disagreement score0.836
Threshold uncertainty score0.970

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.007
Science and technology studies0.0060.003
Scholarly communication0.0050.002
Open science0.0030.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0140.001

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 head0.034
GPT teacher head0.404
Teacher spread0.370 · 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
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

Citations51
Published2018
Admission routes3
Has abstractyes

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