Implementing Publicly Funded Noninvasive Prenatal Testing for Fetal Aneuploidy in Ontario, Canada: Clinician Experiences With a Disruptive Technology
Bibliographic record
Abstract
The last decade has experienced unprecedented uptake of noninvasive prenatal testing (NIPT), creating significant changes in the way prenatal clinicians provide services. Through the lens of social shaping of technology, we examine the effects of the introduction of this technology on the health care system in Ontario, Canada. Using a qualitative descriptive approach, we conducted a cross-sectional study investigating clinicians’ perspectives of NIPT in 2014, 2016, and 2018. Through in-depth interviews (n = 37), we explored their perspectives on the impact of NIPT on their referral practices, workload, coordination of testing modalities, education and counseling, and elicited their views on recent expansions of the test. Findings suggest that the introduction of NIPT has created unintended consequences with respect to clinician workload and wellness, clinician education, equity of access, and public system resources. Responsiveness from decision makers is key to ensuring the responsible use of NIPT in the health care system.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".