Guidance for considering ethical, legal, and social issues in health technology assessment: Application to genetic screening
Bibliographic record
Abstract
OBJECTIVES AND METHODS: Many authors have argued that ethical, legal, and social issues ("ELSIs") should be explicitly integrated into health technology assessment (HTA), yet doing so poses challenges. This discussion may be particularly salient for technologies viewed as ethically complex, such as genetic screening. Here we provide a brief overview of contemporary discussions of the issues from the HTA literature. We then describe key existing policy evaluation frameworks in the fields of disease screening and public health genomics. Finally, we map the insights from the HTA literature to the policy evaluation frameworks, with discussion of the implications for HTA in genetic screening. RESULTS AND CONCLUSIONS: A critical discussion in the HTA literature considers the definition of ELSIs in HTA, highlighting the importance of thinking beyond ELSIs as impacts of technology. Existing HTA guidance on integrating ELSIs relates to three broad approaches: literature synthesis, involvement of experts, and consideration of stakeholder values. The thirteen key policy evaluation frameworks relating to disease screening and public health genomics identified a range of ELSIs relevant to genetic screening. Beyond straightforward impacts of screening, these ELSIs require consideration of factors such as the social and political context surrounding policy decisions. The three broad approaches to addressing ELSIs described above are apparent in the screening/genomics literatures. In integrating these findings we suggest that the method chosen for addressing ELSIs in HTA for genetic screening may determine which ELSIs are prioritized; and that an important challenge is the lack of guidance for evaluating such methods.
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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 | 0.571 | 0.640 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.010 | 0.046 |
| Scholarly communication | 0.031 | 0.034 |
| Open science | 0.013 | 0.022 |
| Research integrity | 0.070 | 0.054 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".