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Record W2621477568 · doi:10.1177/0840470417706378

Impact of language barriers on access to healthcare for official language minority Francophones in Canada

2017· article· en· W2621477568 on OpenAlexaffabout
Danielle de Moissac, Sarah Bowen

Bibliographic record

VenueHealthcare Management Forum · 2017
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsUniversité de Saint-Boniface
Fundersnot available
KeywordsLanguage barrierHealth careFrenchOfficial languageMinority languageQuality (philosophy)Political scienceFace (sociological concept)First languagePublic relationsNursingMedicineSociologyLinguistics

Abstract

fetched live from OpenAlex

While there is strong international evidence that language barriers present obstacles to healthcare access, quality and safety, little research has been conducted on the experience of official language minorities in Canada. This multiple method research used on-line and paper-based surveys, combined with semi-structured individual interviews to explore the experience with access to care of Francophone minorities living in four Canadian provinces. The majority of Francophones surveyed reported limited access to French language services and described an environment where low importance is given to addressing language barriers within the health system. Even when services are available, the lack of services in French sometimes results in avoidance of care. Results confirm that many Francophones face similar barriers to care as other language minorities in Canada. Strategies to improve access for official language minorities are discussed.

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)0.006
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 score0.036
Threshold uncertainty score0.264

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.003
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.046
GPT teacher head0.462
Teacher spread0.416 · 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

Citations74
Published2017
Admission routes2
Has abstractyes

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