Predictors of Belief That Genetic Test Information About Hemochromatosis Should Be Shared with Family Members
Bibliographic record
Abstract
We queried 101,951 white, Hispanic, black, Asian, American Indian (i.e., American Indian or Alaska Native in the United States and North American Indian, Metis, or Inuit in Canada) and Pacific Islander (including Native Hawaiian) adults who agreed to be genotypically and phenotypically screened for hemochromatosis as part of the Hemochromatosis and Iron Overload Screening (HEIRS) study about their views on sharing genetic test information with family members. Multiple logistic regression (adjusting for study site, age group, race/ethnicity, preferred language, gender, education group, income group, SF-36 General Health and Mental Health subscales, perceived benefits and limitations of genetic testing, and belief that genetic testing is a good idea) evaluated independent predictors of responding "Strongly Agree" or "Agree" versus "Disagree" or "Strongly Disagree" to the statement "Information about a person's genetic risk should be shared with family members". Agreement that genetic risk information should be shared with family members was high (93% in the overall sample of 78,952 who answered this question), but differed among racial/ethnic groups. Hispanics were significantly less likely to agree that genetic test information should be shared with family members (i.e., 88% versus 92% or more among all other ethnicities). The relationship of perceived limitations and benefits of testing, gender, and age group to the belief that information should be shared differed among racial/ethnic groups, with Spanish-preferring Hispanics being the most different from other subgroups.
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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 | 0.002 | 0.017 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".