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Record W4381470312 · doi:10.1177/08404704231183599

Disparities in access to primary care are growing wider in Canada

2023· article· en· W4381470312 on OpenAlexafffundabout
M. Ruth Lavergne, Aidan Bodner, Sara Allin, Erin Christian, Mohammad Hajizadeh, Lindsay Hedden, Alan Katz, George Kephart, Myles Leslie, David Rudoler, Sarah Spencer

Bibliographic record

VenueHealthcare Management Forum · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsOntario Tech UniversityUniversity of CalgaryIzaak Walton Killam Health CentreUniversity of ManitobaSimon Fraser UniversityUniversity of TorontoDalhousie University
FundersInstitute of Health Services and Policy ResearchCanadian Institutes of Health Research
KeywordsRacializationImmigrationEquity (law)Metropolitan areaResidenceHealth equityEducational attainmentDemographic economicsHealth careInequalityPrimary careEconomic growthPolitical scienceSociologyGeographyMedicineEconomicsRace (biology)Gender studiesFamily medicine

Abstract

fetched live from OpenAlex

Canadian provinces and territories have undertaken varied reforms to how primary care is funded, organized, and delivered, but equity impacts of reforms are unclear. We explore disparities in access to primary care by income, educational attainment, dwelling ownership, immigration, racialization, place of residence (metropolitan/non-metropolitan), and sex/gender, and how these have changed over time, using data from the Canadian Community Health Survey (2007/08 and 2015/16 or 2017/18). We observe disparities by income, educational attainment, dwelling ownership, recent immigration, immigration (regular place of care), racialization (regular place of care), and sex/gender. Disparities are persistent over time or increasing in the case of income and racialization (regular medical provider and consulted with a medical professional). Primary care policy decisions that do not explicitly consider existing inequities may continue to entrench them. Careful study of equity impacts of ongoing policy reforms is needed.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.924
Threshold uncertainty score0.550

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.010
Science and technology studies0.0070.002
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.058
GPT teacher head0.396
Teacher spread0.338 · 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 designObservational
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

Citations46
Published2023
Admission routes3
Has abstractyes

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