Political Economy, Stakeholder Voices, and Saliency: Lessons From International Policies Regulating Insurer Use of Genetic Information
Bibliographic record
Abstract
A decade ago, Congress passed the Genetic Information Nondiscrimination Act (GINA), with the goals to address fear of genetic discrimination and prevent adverse health insurance and employment decisions on the basis of one’s genetic information. Yet, fear of discrimination remains because other insurers, notably life, long-term care, and disability insurers, are not covered by the law. Therefore, there have been persistent murmurings for a “GINA 2.0” to extend the protections of the original law. Although it is plausible to assume that the insurance industry has the political economy to control future regulation, given the saliency of genetic discrimination, other stakeholders and bureaucrats may have greater influence. This paper explores the history of policy in four countries — the United Kingdom, Sweden, Australia, and Canada. Each country provides examples of continued policy debate and change following an initial period of reliance on insurance industry self-regulation, with change generally occurring over the objection of the insurance industry. This article argues that US insurers, regulators, and stakeholders should negotiate a consensus solution for insurer use of genetic information that balances between social and economic considerations. The international landscape provides a myriad of examples of policies that have been employed to meet these goals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".