MétaCan
Menu
Back to cohort

Community‐Based Breast and Cervical Cancer Control Research in Asian Immigrant Populations

2014· other· en· W4233919834 on OpenAlexaffabout
Victoria M. Taylor, T. Gregory Hislop, Yutaka Yasui, Shin‐Ping Tu, J. Carey Jackson

Bibliographic record

VenueWiley StatsRef: Statistics Reference Online · 2014
Typeother
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsEthnic groupImmigrationMedicineBreast cancerMammographyCervical cancerCommunity-based participatory researchParticipatory action researchRandomized controlled trialGerontologyFamily medicineGynecologyDemographyGeographyCancerPolitical scienceSociologyPathology

Abstract

fetched live from OpenAlex

Abstract Asian‐American women are less likely to receive mammography and Papanicolaou (Pap) testing than women from other racial/ethnic groups. Screening rates are particularly low among Asian women who are not proficient in English. This article focuses on community‐based breast and cervical cancer control research in Asian populations. First, the authors address community participatory research principles and several important program evaluation issues: sampling, survey methods, recruitment and retention, translation, data quality, and control group “contamination.” Second, the authors summarize community‐based studies that aimed to increase mammography or Pap testing levels among women of Asian descent. Overall, 9 relevant studies are identified that targeted Asian communities in Canada, the United States, and England. Overall, three of the studies randomized individual women to intervention or control status, two randomized groups of women, and four used a quasi‐experimental (two‐community) study design. These studies, which reported mixed results, highlight the methodological challenges in conducting evaluative public health research, particularly in racial/ethnic minority communities.

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 categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.813
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.247
GPT teacher head0.459
Teacher spread0.212 · 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.

Study designNot applicable
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
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueWiley StatsRef: Statistics Reference OnlineSame topicGlobal Cancer Incidence and ScreeningFrench-language works237,207