The value of non-invasive prenatal testing: preferences of Canadian pregnant women, their partners, and health professionals regarding NIPT use and access
Bibliographic record
Abstract
BACKGROUND: Canadian policies regarding the implementation and public coverage of non-invasive prenatal testing (NIPT) are heterogeneous and shifting, with NIPT being publicly covered for high-risk pregnancies in some provinces, but not others. Such a diverse and evolving policy landscape provides fertile ground for examining the preferences of pregnant women, their partners, and health professionals regarding the implementation and coverage of NIPT by the public healthcare system, as well as the factors influencing their preferences, which is what the present study does. METHODS: In this paper, we report the results of three-large scale Canadian surveys, in which 882 pregnant women, 395 partners of pregnant women, and 184 healthcare professionals participated. RESULTS: The paper focuses on preferences regarding how and when NIPT should be used, as well as the factors influencing these preferences, and how coverage for NIPT should be provided. These are correlated with respondents' levels of knowledge about Down syndrome and testing technologies and with their stated intended use of NIPT results. CONCLUSION: Salient is the marked difference between the preferences of prospective parents and those of healthcare professionals, which has potential implications for Canadian policy regarding NIPT implementation and insurance coverage.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".