Womenâ s perspectives on the ethical implications of non-invasive prenatal testing: a qualitative analysis to inform health policy decisions
Bibliographic record
Abstract
Abstract Background Non-Invasive Prenatal Testing (NIPT) is a technology which provides information about fetal genetic characteristics (including sex) very early in pregnancy by examining fetal DNA obtained from a sample of maternal blood. NIPT is a morally complex technology that has advanced quickly to market with a strong push from industry developers, leaving many areas of uncertainty still to be resolved, and creating a strong need for health policy that reflects womenâ s social and ethical values. We approach the need for ethical policy-making by studying the use of NIPT and emerging policy in the province of Ontario, Canada. Methods Using an adapted version of constructivist grounded theory, we conducted interviews with 38 women who have had personal experiences with NIPT. We used an iterative process of data collection and analysis and a staged coding strategy to conduct a descriptive analysis of ethics issues identified implicitly and explicitly by women who have been affected by this technology. Results The findings of this paper focus on current ethical issues for women seeking NIPT, including place in the prenatal pathway, health care provider counselling about the test, industry influence on the diffusion of NIPT, consequences of availability of test results. Other issues gain relevance in the context of future policy decisions regarding NIPT, including funding of NIPT and principles that may govern the expansion of the scope of NIPT. These findings are not an exhaustive list of all the potential ethical issues related to NIPT, but rather a representation of the issues which concern women who have personal experience with this test. Conclusions Women who have had personal experience with NIPT have concerns and priorities which sometimes contrast dramatically with the theoretical ethics literature. These findings suggest the importance of engaging patients in ethical deliberation about morally complex technologies, and point to the need for more deliberative patient engagement work in this area.
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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 | 0.026 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".