Anticipating dissemination of cancer genomics in public health: A theoretical approach to psychosocial and behavioral challenges
Bibliographic record
Abstract
BACKGROUND: Given the recent sequencing of the human genome, genetic susceptibility information will probably be increasingly useful in the prevention and control of many common diseases, including cancer. PURPOSE: Although much is known about psychosocial factors related to the impact of cancer genetic testing among high-risk families in specialized clinic settings, much less is known about how genetic susceptibility information may contribute to the health and well-being of the general population. METHODS: We present a theoretical synthesis drawn from the health communication and health behavior change traditions to guide research examining psychosocial and behavioral challenges central to dissemination of cancer genomics in public health. RESULTS: These challenges include (a) anticipating individuals' reactions to receiving genetic information that is probabilistic and derived from multiple sources; (b) modeling the influence of public communication about genetics on the population; (c) confronting the need to disseminate cancer genomic information through public health channels; and (d) maximizing opportunities to achieve cancer risk reduction across individuals, families, and local environments. Throughout the article, we use melanoma genomics as an example of the issues requiring attention. CONCLUSIONS: We hope the model helps shape the psychosocial and behavioral research agenda concerning the impact of cancer genomics outside the high-risk clinic.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".