Help or hindrance: young people's experiences of predictive testing for Huntington's disease
Bibliographic record
Abstract
A growing number of young people (YP) are requesting predictive testing (PT) for Huntington's disease (HD), yet there is little research in this area. The aim of this study was to explore YP's experiences of PT for HD, the impact of their result and any gaps in information or support. In-depth interviews were conducted with YP who sought PT for HD from nationally funded Genetics Services. Participants were recruited through the Grampian Genetics Service or Scottish Huntington's Association. Twelve female participants aged 17-26 years were recruited (seven below 20 years). Pre- and post-test interviews were conducted where possible. A qualitative thematic analysis suggests three main testing experiences, regardless of test result. Testing may be: (i) a journey of empowerment, (ii) an ambivalent process or (iii) a poor experience. In pre-test counselling, gaps in emotional support were highlighted. The post-test period was particularly difficult if there were unanticipated changes in family dynamics or an individual's result contradicted what they expected 'deep down'. YP's experiences of PT for HD are generally similar to those of adults, but testing may help or interfere with key issues related to this age and stage. Implications for clinical practice are outlined.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".