Ethical, Legal, and Social Issues in Health Technology Assessment for Prenatal/Preconceptional and Newborn Screening: A Workshop Report
Bibliographic record
Abstract
Prenatal/preconceptional and newborn screening programs have been a focus of recent policy debates that have included attention to ethical, legal, and social issues (ELSIs). In parallel, there has been an ongoing discussion about whether and how ELSIs may be addressed in health technology assessment (HTA). We conducted a knowledge synthesis study to explore both guidance and current practice regarding the consideration of ELSIs in HTA for prenatal/preconceptional and newborn screening. As the concluding activity for this project, we held a Canadian workshop to discuss the issues with a diverse group of stakeholders. Based on key workshop themes integrated with our study results, we suggest that population-based genetic screening programs may present particular types of ELSIs and that a public health ethics perspective is potentially highly relevant when considering them. We also suggest that approaches to addressing ELSIs in HTA for prenatal/preconceptional and newborn screening may need to be flexible enough to respond to diversity in HTA organizations, cultural values, stakeholder communities, and contextual factors. Finally, we highlight a need for transparency in the way that HTA producers move from evidence to conclusions and the ways in which screening policy decisions are made.
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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 | 0.090 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".