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Record W2093948180 · doi:10.3390/ijerph9103755

Experiences of French Speaking Immigrants and Non-immigrants Accessing Health Care Services in a Large Canadian City

2012· article· en· W2093948180 on OpenAlexafffundabout
Emmanuel Ngwakongnwi, Brenda R. Hemmelgarn, Richard Musto, Hude Quan, Kathryn King‐Shier

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2012
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health ResearchAlberta Innovates
KeywordsImmigrationInterpreterLanguage barrierHealth careDistressMedicineEthnic groupFrenchNursingPsychologyFamily medicinePolitical scienceGeographyLaw

Abstract

fetched live from OpenAlex

French speakers residing in predominantly English-speaking communities have been linked to difficulties accessing health care. This study examined health care access experiences of immigrants and non-immigrants who self-identify as Francophone or French speakers in a mainly English speaking province of Canada. We used semi-structured interviews to gather opinions of recent users of physician and hospital services (N = 26). Language barriers and difficulties finding family doctors were experienced by both French speaking immigrants and non-immigrants alike. This was exacerbated by a general preference for health services in French and less interest in using language interpreters during a medical consultation. Some participants experienced emotional distress, were discontent with care received, often delayed seeking care due to language barriers. Recent immigrants identified lack of insurance coverage for drugs, transportation difficulties and limited knowledge of the healthcare system as major detractors to achieving health. This study provided the groundwork for future research on health issues of official language minorities in Canada.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.107
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.096
GPT teacher head0.477
Teacher spread0.380 · 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.

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

Citations36
Published2012
Admission routes3
Has abstractyes

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