Pregnant women's and policymakers' preferences for the expansion of noninvasive prenatal screening: A discrete choice experiment approach study
Bibliographic record
Abstract
Background and Aims: Quantitative approaches for eliciting preferences for new interventions are mostly conducted by patients and rarely by policymakers. This study aimed to quantify the preferences of pregnant women and policymakers regarding the addition of a new test to prenatal screening programs for detecting chromosomal abnormalities. Methods: A discrete choice experiment was conducted to measure the respondents' preferences for a new prenatal test. A seven-attribute instrument was built based on interviews with pregnant women and policymakers. The data were analyzed using robust conditional logistic regression and nested logit models. Results: In total, 272 pregnant women and 24 policymakers completed the questionnaire (response rates of 48% and 55%, respectively). Overall, all attributes were statistically significant in the pregnant women group, whereas only three attributes (test performance, degree of test result certainty, and cost) were statistically significant in the policymakers group. Statistically significant differences in test performance and information were observed between the two groups. Conclusion: Policymakers differed from pregnant women in their appraisal of attributes related to their preference for a new prenatal screening intervention. The low response rates observed in both groups suggest that further investigation of the relevance of this approach must be conducted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".