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Record W4323050196 · doi:10.1007/s10903-023-01459-4

Sex, Immigration, and Patterns of Access to Primary Care in Canada

2023· article· en· W4323050196 on OpenAlexafffundabout
Joseph M. Ssendikaddiwa, Shira M. Goldenberg, Nicole S. Berry, M. Ruth Lavergne

Bibliographic record

VenueJournal of Immigrant and Minority Health · 2023
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsDalhousie UniversitySimon Fraser University
FundersCanadian Institutes of Health ResearchNational Institutes of HealthNational Institute on Drug AbuseCanada Research ChairsFoundation for the National Institutes of Health
KeywordsImmigrationOddsLogistic regressionPrimary carePublic healthMedicineHealth careDemographyOdds ratioGerontologyFamily medicineNursingPolitical scienceSociology

Abstract

fetched live from OpenAlex

Access to primary care is crucial to immigrant health and may be shaped by sex and gender, but research is limited and inconclusive. We identified measures that reflect access to primary care using 2015-2018 Canadian Community Health Survey data. We used multivariable logistic regression models to estimate adjusted odds of primary care access and to explore interaction effects between sex and immigration group (recent immigrant: < 10 years in Canada, long-term immigrant: 10 + years, non-immigrant). Recency of immigration and being male were negatively associated with access to primary care, with significantly lower odds of having a usual place for immediate care among male recent immigrants (AOR: 0.36, 95% CI 032-0.42). Interaction effects between immigration and sex were pronounced, especially for having a regular provider or place of care. Results underscore the need to examine approachability and acceptability of primary care services, especially for male recent immigrants.

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)0.004
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 score0.025
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0050.001
Scholarly communication0.0020.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.320
Teacher spread0.296 · 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

Citations2
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Immigrant and Minority HealthSame topicMigration, Health and TraumaFrench-language works237,207