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Record W2166269243 · doi:10.1007/s10897-008-9202-z

Experiences and Decisions that Motivate Women at Increased Risk of Breast Cancer to Participate in an Experimental Screening Program

2009· article· en· W2166269243 on OpenAlexaffabout
Michelle Proulx, Marie‐Dominique Beaulieu, Christine Loignon, Marie‐Hélène Mayrand, Christine Maugard, Nathalie Bellavance, Diane Provencher

Bibliographic record

VenueJournal of Genetic Counseling · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsHôtel-Dieu de MontréalMcGill UniversityHôpital Notre-DameUniversité de Montréal
Fundersnot available
KeywordsMedicineBreast cancerDocumentationBreast cancer screeningFamily medicinePsychological interventionQualitative researchPublic healthHealth professionalsGenetic counselingGenetic testingGynecologyCancerMammographyNursingHealth careInternal medicineGenetics

Abstract

fetched live from OpenAlex

Although the discovery of mutations on BRCA1 and BRCA2 genes associated with high breast cancer risk has given rise to screening and surveillance initiatives, there is little documentation on why high-risk women choose to enter screening programs. The objective of this qualitative study was to develop a detailed understanding of the experiences and decisions that motivate women with increased risk of hereditary breast cancer to participate in the multicentered Quebec experimental breast screening program. Our study involved 21 participants who were either BRCA carriers or at risk and untested. These women were interviewed while participating in the screening program. Our study demonstrates that intensive screening programs may provide valuable reassurance for women with increased familial risk of hereditary breast cancer, who count on early detection and rapid response from professionals if and when a problem arises. Health professionals must take these and others concerns into account to ensure their interventions are most consistent with women's needs.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.022
GPT teacher head0.327
Teacher spread0.305 · 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 designBench or experimental
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

Citations12
Published2009
Admission routes2
Has abstractyes

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