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Record W7027022341

Breast and cervical cancer screening : knowledge, attitudes, and practices of Vietnamese Canadian women living in Toronto, Ontario / by Tue Tran Nghi Nguyen.

2017· other· en· W7027022341 on OpenAlexaboutno aff

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2017
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsVietnameseCervical cancerBreast cancerCervical cancer screeningCancerBreast cancer screeningEthnic groupCancer screening
DOInot available

Abstract

fetched live from OpenAlex

Breast cancer is the most common cancer, and cervical cancer is the second most
\ncommon cancer, among Vietnamese women in North America. This ranking replicates
\nthe order of cancer prevalence among women residing in Vietnam. Unfortunately,
\nUnfortunately, Vietnamese women are less likely to report ever having had recommended
\nscreening procedures for these cancers and are more likely to be overdue for them than
\nwomen in the general populations (Miller, Kolonel, & Bernstein, 1996). Many factors
\nhave been highlighted from previous studies to shed light into this cancer prevalence.
\nThese factors include but not limited to accessibility of service, lack of knowledge on
\nbreast and cervical cancers, inadequate number of female physicians, language barrier,
\netc. Therefore, the overall purpose of this project is to examine the knowledge, attitudes,
\nand practices of Vietnamese Canadian women ages 40 to 60 toward breast and cervical
\ncancer screenings.
\n
\nObjectives of the Study:
\n
\n1. To identify the possible barriers in race or ethnicity, culture, and socioeconomic
\nstatus (SES) that Vietnamese Canadian women face in their efforts to take preventive
\nmeasures and participate in breast and cervical cancer screening.
\n2. To make recommendations and develop strategies for ethnic-focused breast and
\ncervical cancer health promotion.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.625
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.281
Teacher spread0.258 · 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 designObservational
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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