MétaCan
Menu
Back to cohort

Healthcare Inaccessibility of Asian Females in Canada: Barriers to Cancer Preventative Screening

2023· article· en· W4389394529 on OpenAlexaffabout
Yutong Lu

Bibliographic record

VenueLecture Notes in Education Psychology and Public Media · 2023
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsInterpersonal communicationHealth careContext (archaeology)ImmigrationSocioeconomic statusEthnic groupBreast cancer screeningBreast cancerMedicinePsychological interventionLanguage barrierFamily medicinePsychologyNursingMammographyCancerPolitical scienceEnvironmental healthGeographyPopulationSocial psychology

Abstract

fetched live from OpenAlex

Breast cancer represents a significant issue within the Canadian context, particularly as it pertains to Asian immigrant women who experience comparatively lower rates of mammographic screening. This literature review examines the barriers that impede Asian immigrant women from accessing mammographic screening, with the exception of socioeconomic status, which has been extensively investigated in previous research. The review concludes that the barriers are primarily associated with physician-patient communication and can be categorized into three themes: linguistic, cultural, and knowledge-related aspects. Potential solutions encompass the implementation of customized health education campaigns and the provision of cross-cultural healthcare services, with a particular emphasis on adopting a patient-centered approach to interpersonal contact between healthcare providers and individuals seeking medical care. By effectively tackling these complex problems, interventions have the potential to increase knowledge, facilitate interpersonal exchange, and promote equal opportunities for mammographic screening among Asian immigrant women. This, in turn, can lead to a reduction in the overall burden of breast cancer.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.468
Threshold uncertainty score0.781

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.098
GPT teacher head0.443
Teacher spread0.345 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueLecture Notes in Education Psychology and Public MediaSame topicGlobal Cancer Incidence and ScreeningFrench-language works237,207