In their own words: Reports of stigma and genetic discrimination by people at risk for Huntington disease in the International RESPOND‐HD study
Bibliographic record
Abstract
Genetic discrimination may be experienced in the day-to-day lives of people at risk for Huntington disease (HD), encompassing occurrences in the workplace, when seeking insurance, within social relationships, and during other daily encounters. At-risk individuals who have tested either positive or negative for the genetic expansion that causes HD, as well as at-risk persons with a 50% chance for developing the disorder but have not had DNA testing completed the International RESPOND-HD (I-RESPOND-HD) survey. One of the study's purposes was to examine perceptions of genetic stigmatization and discrimination. A total of 412 out of 433 participants provided narrative comments, and 191 provided related codable narrative data. The core theme, Information Control, refers to organizational policies and interpersonal actions. This theme was found in narrative comments describing genetic discrimination perceptions across employment, insurance, social, and other situations. These reports were elaborated with five themes: What They Encountered, What They Felt, What Others Did, What They Did, and What Happened. Although many perceptions were coded as hurtful, this was not true in all instances. Findings document that reports of genetic discrimination are highly individual, and both policy as well as interpersonal factors contribute to the outcome of potentially discriminating events.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".