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Record W4414300685 · doi:10.12927/hcpol.2025.27664

Healthcare Access Gaps Persist for French-Preferring Citizens in Canada’s Only Officially Bilingual Province: Analysis of New Brunswick Patient Care Experience Survey Data

2025· article· en· W4414300685 on OpenAlexaffvenueabout
François Gallant, Lise Babin, James Ted McDonald

Bibliographic record

VenueHealthcare policy · 2025
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsUniversity of New BrunswickUniversité de SherbrookeDalhousie University
Fundersnot available
KeywordsHealth careContext (archaeology)Quality (philosophy)Primary careSurvey data collectionLanguage barrierPatient careData collection

Abstract

fetched live from OpenAlex

Language differences between patients and care providers are a major barrier to delivering quality healthcare. We describe citizen-reported access to healthcare in their preferred official language in New Brunswick by examining survey data from the New Brunswick Health Council (2021 and 2023). Nearly all New Brunswickers report access to a primary care provider in their preferred official language, but other sectors of primary care (e.g., pharmacy, specialists, telehealth) represent significant challenges for French-preferring citizens. Given New Brunswick's unique context as Canada's only officially bilingual province, we highlight research opportunities that could inform strategies to improve language-concordant healthcare nationally.

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)0.003
metaresearch head score (Gemma)0.010
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 score0.064
Threshold uncertainty score0.461

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.012
Science and technology studies0.0050.002
Scholarly communication0.0040.001
Open science0.0020.003
Research integrity0.0010.001
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 head0.163
GPT teacher head0.501
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

Citations1
Published2025
Admission routes3
Has abstractyes

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