Public Perceptions of the Benefits and Risks of Newborn Screening
Bibliographic record
Abstract
BACKGROUND: Growing technological capacity and parent and professional advocacy highlight the need to understand public expectations of newborn population screening. METHODS: We administered a bilingual (French, English) Internet survey to a demographically proportional sample of Canadians in 2013 to assess preferences for the types of diseases to be screened for in newborns by using a discrete choice experiment. Attributes were: clinical benefits of improved health, earlier time to diagnosis, reproductive risk information, false-positive (FP) results, and overdiagnosed infants. Survey data were analyzed with a mixed logit model to assess preferences and trade-offs among attributes, interaction between attributes, and preference heterogeneity. RESULTS: On average, respondents were favorable toward screening. Clinical benefits were the most important outcome; reproductive risk information and early diagnosis were also valued, although 8% disvalued early diagnosis, and reproductive risk information was least important. All respondents preferred to avoid FP results and overdiagnosis but were willing to accept these to achieve moderate clinical benefit, accepting higher rates of harms to achieve significant benefit. Several 2-way interactions between attributes were statistically significant: respondents were willing to accept a higher FP rate for significant clinical benefit but preferred a lower rate for moderate benefit; similarly, respondents valued early diagnosis more when associated with significant rather than moderate clinical benefit. CONCLUSIONS: Members of the public prioritized clinical benefits for affected infants and preferred to minimize harms. These findings suggest support for newborn screening policies prioritizing clinical benefits over solely informational benefits, coupled with concerted efforts to avoid or minimize harms.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".