An Exploration of Attitudes Among Black Americans Towards Psychiatric Genetic Research
Bibliographic record
Abstract
With increasing emphasis on understanding genetic contribution to disease, inclusion of all racial and ethnic groups in molecular genetic research is necessary to ensure parity in distribution of research benefits. Blacks are underrepresented in large-scale genetic studies of psychiatric disorders. In an effort to understand the reasons for the underrepresentation, this study explored black participants' attitudes towards genetic research of psychiatric disorders. Twenty-six adults, the majority of whom were black (n = 18) were recruited from a New York City community to participate in six 90-minute focus groups. This paper reports findings about respondents' understanding of genetics and genetic research, and opinions about psychiatric genetic research. Primary themes revealed participants' perceived lack of knowledge about genetics, concerns about potentially harmful study procedures, and confidentiality surrounding mental illness in families. Participation incentives included provision of treatment or related service, monetary compensation, and reporting of results to participants. These findings suggest that recruitment of subjects into genetic studies should directly address procedures, privacy, benefits and follow-up with results. Further, there is critical need to engage communities with education about genetics and mental illness, and provide opportunities for continued discussion about concerns related to genetic research.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".