Beyond the patient: The broader impact of genetic discrimination among individuals at risk of Huntington disease
Bibliographic record
Abstract
We aimed to address gaps in current understanding of the scope and impact of discrimination, by examining a cohort of individuals at-risk for Huntington disease (HD), to describe the prevalence of concern for oneself and one's family in multiple domains; strategies used to mitigate discrimination; and the extent to which concerns relate to experiences. We conducted a cross-sectional survey of 293 individuals at-risk for HD (80% response rate); 167 respondents were genetically tested and 66 were not. Fear of discrimination was widespread (86%), particularly in the insurance, family and social settings. Approximately half of concerned individuals experienced discrimination (40-62%, depending on genetic status). Concern was associated with "keeping quiet" about one's risk of HD or "taking action to avoid" discrimination. Importantly, concern was highly distressing for some respondents (21% for oneself; 32% for relatives). Overall, concerned respondents with high education levels, who discovered their family history at a younger age, and those who were mutation-positive were more likely to report experiences of discrimination than others who were concerned. Concerns were rarely attributed to genetic test results alone. Concern about genetic discrimination is frequent among individuals at-risk of HD and spans many settings. It influences behavioral patterns and can result in high levels of self-rated distress, highlighting the need for practice and policy interventions. © 2012 Wiley Periodicals, Inc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".