Perceptions of discrimination among persons who have undergone predictive testing for Huntington's disease
Bibliographic record
Abstract
Potential discrimination from genetic testing may undermine technological advances for health care. Researching long-term consequences of testing for genetic conditions that may lead to discrimination is a public health priority. The consequences of genetic discrimination generate social, health, and economic burdens for society by diminishing opportunities for at-risk individuals in a range of contexts. The current study objective was to investigate perceptions of genetic stigmatization and discrimination among persons who completed predictive testing for Huntington's disease (HD). Using semi-structured interviews and computerized qualitative analysis, the perceptions of 15 presymptomatic persons with a positive gene test predicting HD were examined with regard to differential treatment following testing. The sample comprised 11 women and 4 men, mostly married (73%), aged between 22 and 62 years, with an average education of 14.6 years (SD +/- 2.57) and residing in urban, rural and suburban settings of eight U.S. States. Participants reported perceptions of consequences following disclosure of genetic test results in three areas: employment, insurance, and social relationships. Although most employed participants (90%) revealed their test results to their employers, nearly all reported they would not disclose this information to future employers. Most (87%) participants disclosed test results to their physician, but a similar majority (83%) did not tell their genetic status to insurers. Most participants (87%) disclosed test results to family and peers; patterns of disclosure varied widely. Discrimination concerns remain high in this sample and point to the need for more information to determine the extent and scope of the problem.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".