Impact of genetic testing on causal models of heart disease and arthritis: An analogue study
Bibliographic record
Abstract
Abstract An analogue study investigated the impact of genetic testing on perceptions of disease. Using a 2 × 2 design, participants (n = 212) imagined receiving the information that they were at increased risk for either heart disease or arthritis. The type of risk information was either genetic or unspecified. Presentation of genetic risk information resulted in the condition being perceived as less preventable. Causal models of disease where investigated using principal components analysis. When hem disease was the stimulus condition, attributions to genes and chance were positively associated following unspecified risk information, and negatively associated following genetic risk information. When arthritis was the stimulus condition, presentation of genetic risk information was associated with attributions to genes becoming separated from the other attributions. One explanation for this is that providing genetic risk information may decrease perceptions of a sense of randomness or uncertainty in disease causation. The extent to which these effects occur in clinical populations. and their behavioural consequences. needs to be established.
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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 | 0.005 | 0.046 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".