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Record W2074640131 · doi:10.5993/ajhb.39.3.2

Cervical Cancer Screening Behavior among Hmong-American Immigrant Women

2015· article· en· W2074640131 on OpenAlexaff
Hee Yun Lee, Pa Nhia Yang, Do Kyung Lee, Rahel Ghebre

Bibliographic record

VenueAmerican Journal of Health Behavior · 2015
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsWomen's Health Research Institute
Fundersnot available
KeywordsPap testFatalismMedicineImmigrationCervical cancerMarital statusCancer screeningLogistic regressionTest (biology)Health literacyAcculturationPopulationDemographyReceiptCancerGerontologyCervical cancer screeningGynecologyEnvironmental healthHealth careGeographyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To investigate Hmong-American immigrant women's utilization of cervical cancer screening, including the impact of cultural health beliefs on screening use. METHODS: Overall, 164 Hmong-American immigrant women 21 to 65 years of age were recruited from a large metropolitan area in the Midwest. We used logistic regression, guided by Andersen's Behavior Model, to examine factors associated with the receipt of Pap test. RESULTS: About 67.1% had received a Pap test within the last 3 years. Fatalism, modesty, education, and marital status were significantly correlated with receiving a Pap test. CONCLUSION: The provision of cervical cancer literacy education and related preventive guidelines to this population are urgently needed to reduce cancer-screening disparity.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.097
GPT teacher head0.410
Teacher spread0.312 · 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

Citations19
Published2015
Admission routes1
Has abstractyes

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