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Record W7132943616

Implications of English Proficiency on Immigrant Women's Access to & Utilization of Health Services

2009· article· en· W7132943616 on OpenAlexafffundabout
Sepali Guruge, Rachel Berman, Vappu Tyyskä, Kenise Murphy Killbride, Isaac Woungang, Susanna Edwards, Laurie Clune

Bibliographic record

VenueTSpace · 2009
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsToronto Metropolitan University
FundersCanadian Council on Learning
KeywordsImmigrationSettlement (finance)Health careLanguage barrierHealth servicesQualitative researchService providerLimited English proficiencyService (business)
DOInot available

Abstract

fetched live from OpenAlex

Having the capacity to communicate in a common language is centrally important when people access and utilize health, social, and settlement services. While a considerable portion of recent immigrant women to Canada speak English or French fluently, many others do not command either official language fluently enough to access and utilize available health care services. In this qualitative study, data were collected with service providers who work with newcomer women, with women who became fluent in English after arriving in Canada, and with women who do not currently speak English. We report on the challenges women face in acquiring proficiency in English and, through the use of a social determinants of health framework, on how limited language skills negatively influence the health of these immigrant women and their families. We also present a number of strategies that health professionals could use to better support women’s attempts to ensure their health and that of their families.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.289

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.003
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
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 head0.176
GPT teacher head0.551
Teacher spread0.376 · 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

Citations0
Published2009
Admission routes3
Has abstractyes

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