MétaCan
Menu
Back to cohort
Record W6958463779 · doi:10.6084/m9.figshare.27291465

Supplementary Material for: “Should I let them know I have this?”: Multifaceted genetic discrimination and limited awareness of legal protections amongst individuals with hereditary cancer syndromes

2024· dataset· en· W6958463779 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2024
Typedataset
Languageen
FieldArts and Humanities
TopicLibraries and Information Services
Canadian institutionsnot available
Fundersnot available
KeywordsGenetic discriminationHarmGenetic counselingGenetic testingLegislationOutreachStigma (botany)Qualitative research

Abstract

fetched live from OpenAlex

Introduction: Hereditary cancer syndromes (HCS), such as Hereditary Breast and Ovarian Cancer Syndrome (HBOC) and Lynch Syndrome (LS), represent approximately 10% of all cancers. Along with medical burdens associated with the genetic risk of developing cancer, many individuals face stigma and discrimination. Genetic discrimination refers to negative treatment, unfair profiling or harm based on genetic characteristics, manifesting as “felt” stigma (ostracization without discriminatory acts) or “enacted” stigma (experiencing discriminatory acts). This study aimed to describe concerns and experiences of genetic discrimination faced by individuals with HCS. Methods: Semi-structured qualitative interviews were conducted with individuals with molecularly-confirmed HCS residing in Ontario, British Columbia and Newfoundland & Labrador, Canada. Purposive sampling was applied to obtain a diverse sample across demographic characteristics. Study procedures were informed by interpretive description; data were thematically analyzed using constant comparison. Results: 73 participants were interviewed (39 HBOC, 34 LS; 51 females, 21 males, 1 gender-diverse; aged 25-80). Participants described multifaceted forms of genetic discrimination across healthcare, insurance, employment, and family/social settings. Participants valued the Genetic Non-Discrimination Act’s protective intent, but demonstrated limited knowledge of its existence and provisions. Limited knowledge, coupled with policy constraints in non-legislatable settings and third-party use of proxy genetic information, hindered participants’ ability to whistleblow or seek recourse. Conclusion: Our results illuminate a disconnect between intended protective effects of genetic non-discrimination legislation and ongoing genetic discrimination faced by individuals with hereditary conditions. To better support these individuals, this study encourages public outreach and knowledge translation efforts to increase awareness of non-discrimination legal protections.

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)0.002
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.599
Threshold uncertainty score0.571

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.5990.093

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 head0.074
GPT teacher head0.276
Teacher spread0.201 · 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.

Study designQualitative
Domainnot available
GenreDataset

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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueFigshareSame topicLibraries and Information ServicesFrench-language works237,207