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Record W1971791435 · doi:10.1089/jwh.2009.1477

Prevalence and Factors Associated with Colorectal Cancer Screening in Canadian Women

2010· article· en· W1971791435 on OpenAlexaffabout
Sarah Brennenstuhl, Esme Fuller‐Thomson, Svetlana Popova

Bibliographic record

VenueJournal of Women s Health · 2010
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsCentre for Addiction and Mental HealthUniversity of TorontoPublic Health Ontario
Fundersnot available
KeywordsMedicineLogistic regressionBehavioral Risk Factor Surveillance SystemColorectal cancerCancer screeningGerontologyOdds ratioOddsColorectal cancer screeningDemographyCancerFamily medicineEnvironmental healthPopulationColonoscopyInternal medicine

Abstract

fetched live from OpenAlex

AIMS: This representative study investigated the prevalence and factors associated with women's use of colorectal cancer (CRC) screening. METHODS: Women aged 50-74 were selected from the Ontario subsample of the 2005 Canadian Community Health Survey. Women who had never screened for CRC (n = 3676) were compared with women who had ever had CRC screening (n = 2105). Andersen's Behavioral Model of Health Service Use of predisposing, need, and enabling factors guided chi-square and logistic regression analyses. RESULTS: Fully 38% of women reported ever having CRC screening. Predisposing factors (older age, higher education, white race, currently having cancer [not CRC], and use of other screening tests), need factors (nonsmoking and physical activity level), and enabling factors (urban location, having a family doctor, more than five doctor visits annually, and greater sense of belonging), were each associated with higher odds of screening. Lower household income seemed to be associated with lower odds of screening. CONCLUSIONS: Despite CRC guidelines published before and during 2004, only 38% of women reported ever having CRC screening by 2005. Enabling factors contributed significantly to screening rates after adjustment for need factors, which provides some evidence that access to CRC screening by women was inequitable despite Canada's publicly funded healthcare system. Research findings can help develop female-specific strategies to increase rates of CRC screening.

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)0.001
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 score0.298
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.018
GPT teacher head0.292
Teacher spread0.274 · 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

Citations15
Published2010
Admission routes2
Has abstractyes

Explore more

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