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A gift or a yoke? Women’s and men’s responses to genetic risk information from BRCA1 and BRCA2 testing

2006· article· en· W2140210799 on OpenAlexaff
Lori d’Agincourt-Canning

Bibliographic record

VenueClinical Genetics · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsChildren's & Women's Health Centre of British Columbia
Fundersnot available
KeywordsAgency (philosophy)Genetic testingSocial psychologyIdentity (music)KinshipSense of agencyPsychologyQualitative researchPersonal identityDevelopmental psychologyMedicineSelfSociology

Abstract

fetched live from OpenAlex

This qualitative study explored the impact of genetic risk information from BRCA1/2 testing on individuals' subjective understandings of self and self-identity. In-depth interviews were conducted with 39 participants (34 women and 5 men) who had received test results from BRCA1/2 testing. Themes emerging from qualitative data analysis revealed that participants linked their positive results to becoming more aware of their physical selves (embodied self), their selves in relation to family (familial-relational self) and their selves in relation to wider kinship or social groups (social self). Genetic information was generally viewed as enabling; it allowed participants to take measures (surveillance or prophylactic surgery) to confront the disease. However, for a small minority of women, knowledge about their genetic risk had a profound and limiting effect on their agency. Rather than giving them a sense of control, they saw little opportunity to fight the disease. For a few people, identification of a genetic mutation thrust them into an uncertain state, that is in a position of being neither ill nor completely well. In one case, BRCA information led to a disruption of social identity. Further work is needed to assess the impact of age and life stage on psychological responses to genetic information on cancer susceptibility.

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)0.002
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 score0.255
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.028
GPT teacher head0.324
Teacher spread0.296 · 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

Citations32
Published2006
Admission routes1
Has abstractyes

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