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Record W2107053222 · doi:10.1177/0193945908319247

Measuring Breast Cancer and Mammography Screening Beliefs Among Chinese American Immigrants

2008· article· en· W2107053222 on OpenAlexaff
Frances Lee‐Lin, Usha Menon, Marjorie A. Pett, Lillian M. Nail, Sharon Lee, Kathi Mooney

Bibliographic record

VenueWestern Journal of Nursing Research · 2008
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMammographyImmigrationMedicineBreast cancerBreast cancer screeningCancerMammography screeningChinese americansFamily medicineObstetricsGynecologyInternal medicinePolitical science

Abstract

fetched live from OpenAlex

Disparities in breast cancer outcomes persist among Asian American women. Breast cancer is the most commonly diagnosed cancer among Chinese American women. This article describes the psychometric evaluation of an instrument measuring knowledge and beliefs related to breast cancer and screening among Chinese American women aged 40 or older. A sample of 100 foreign-born Chinese American women were recruited from an Asian community. Guided by the health belief model, a questionnaire was adapted from three existing questionnaires. Principal axis factoring analyses yielded a three-factor solution that accounted for 53% of the variance in the breast cancer items and a four-factor solution that accounted for 69% of the variance in the cultural items (Cronbach's alphas = .71-.89). Whereas these findings contribute to the understanding of the psychometric properties of an instrument targeted for Chinese American women, additional research is needed to evaluate its utility and efficacy for other Asian Americans.

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 score0.091
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.190
GPT teacher head0.437
Teacher spread0.247 · 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

Citations23
Published2008
Admission routes1
Has abstractyes

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