Factors associated with experiences of genetic discrimination among individuals at risk for huntington disease
Bibliographic record
Abstract
The purpose of this study was to identify factors that are associated with experiencing genetic discrimination (GD) among individuals at risk for Huntington disease (HD). Multivariable logistic regression analysis was used to examine factors associated with experiencing GD in data from a cross-sectional, self-report survey of 293 individuals at risk for HD. The study sample comprised 167 genetically tested respondents, and 66 who were not tested (80% response rate). Overall, individuals who learn they are at risk for HD at a younger age (OR = 3.1; 95% CI: 1.5-6.2; P = 0.002), are mutation-positive (OR = 2.8; 95% CI: 1.4-6.0; P = 0.006), or are highly educated (OR = 2.7; 95% CI: 1.4-5.1; P = 0.002) are more likely to experience GD, particularly in insurance, family, and social settings. Further, younger age was associated with discrimination in insurance (OR = 0.97; 95% CI: 0.94-1.00; P = 0.038). This study provides evidence that some people who are at risk for HD were more likely to experience GD than others. Individuals who learned they are at risk for HD at a younger age and those who are mutation-positive were more likely to experience GD, particularly in insurance, family, and social settings. Younger individuals were more likely to experience discrimination in the insurance setting. Overall, highly educated individuals were also more likely to report discrimination. These results provide direction for clinical and family discussions, counseling practice, and policy aimed at mitigating experiences of GD.
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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".