Bibliographic record
Abstract
Research on genetic decision-making normally constructs the decision as an opportunity for choice. However, minimal research investigates how these decisions are taken and whether those who live with genetic risk perceive the test as an opportunity for choice. Employing semistructured interviews with at-risk persons, this study explored decisions about genetic testing for Huntington's disease (HD)--a fatal genetic disorder. A primary aim was to understand how test decisions were perceived. Qualitative data analysis revealed four decision pathways: (1) no decision to be made, (2) constrained decisions, (3) reevaluating the decision, and (4) indicators of HD. Contrary to the rational, "information-processor" approach to decision making, some test decisions were immediate and automatic. These stories challenged the conventional construction of a genetic-test decision as an opportunity for choice. Participant narratives suggested that this construction may be inadequate, at least for some people who live with genetic risk. Test decisions were sometimes constrained by perceived responsibility to other family members, notably offspring. For others at risk, the test decision was a dynamic process of critical thought and evaluation. Finally, behaviors that could be symptoms of HD were the catalyst for testing.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| 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".