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Record W6981212788

Do cancer screening behaviours differ between immigrant populations and Canadian-born citizens?

2022· article· en· W6981212788 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNuclear Structure and Function
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationCancer screeningLogistic regressionPsychological interventionProstate cancer screeningConfoundingNational Health Interview SurveyColorectal cancer screening
DOInot available

Abstract

fetched live from OpenAlex

Effective screening behaviours have been linked with early detection of disease, allowing for prompt treatment of disease. Poor access to health care resources has been documented among immigrant populations. A large, population-based examination of screening behaviours among Canadian immigrants is currently lacking.\nThis analysis seeks to examine how screening behaviours differ between immigrant populations and Canadian-born citizens. Screening behaviours to be investigated are: pap smear, mammography, prostate cancer screening, and colorectal cancer screening. Construction of a multivariable logistic regression model will allow relative rates of screening to be compared after adjusting for all potential confounders in order to elucidate the impact of immigration status on screening behaviours.\nThis analysis will make use of data from the Canadian Community Health Survey (CCHS), 2014. This survey collected information from a representative sample of households across Canada.\nInformation gained from this analysis will determine whether there is a need for targeted interventions to improve screening behaviours within immigrant populations.

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)0.000
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 score0.076
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.070
GPT teacher head0.302
Teacher spread0.232 · 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
Published2022
Admission routes1
Has abstractyes

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