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Record W4389304104 · doi:10.1503/cmaj.221824

Primary care for all: lessons for Canada from peer countries with high primary care attachment

2023· article· en· W4389304104 on OpenAlexafffundvenueabout
Heba Shahaed, Richard H. Glazier, Michael Anderson, Erica Barbazza, Véronique Bos, Ingrid Sperre Saunes, Juha Auvinen, Maryam Daneshvarfard, Tara Kiran

Bibliographic record

VenueCanadian Medical Association Journal · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsInstitute for Work & HealthUniversity of TorontoSt. Michael's Hospital
FundersCanadian Institutes of Health ResearchNational Institute for Health and Care Research
KeywordsPrimary careEquity (law)PandemicPrimary health careCoronavirus disease 2019 (COVID-19)MedicineMEDLINEFamily medicineNursingPolitical scienceEnvironmental healthDiseaseInfectious disease (medical specialty)Pathology

Abstract

fetched live from OpenAlex

KEY POINTS Health systems with strong primary care have better outcomes, lower costs and better equity.[1][1] Yet, even at the outset of the COVID-19 pandemic, about 17% of people in Canada reported not having a regular primary care clinician.[2][2] At the same time, Canada is seeing declining

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.355
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.025
GPT teacher head0.360
Teacher spread0.334 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations27
Published2023
Admission routes4
Has abstractyes

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