Consent for Newborn Screening: The Attitudes of Health Care Providers
Bibliographic record
Abstract
BACKGROUND: As newborn screening (NBS) expands to meet a broader definition of benefit, the scope of parental consent warrants reconsideration. METHODS: We conducted a mixed methods study of health care provider attitudes toward consent for NBS, including a survey (n = 1,615) and semi-structured interviews (n = 36). RESULTS: Consent practices and attitudes varied by provider but the majority supported mandatory screening (63.4%) and only 36.6% supported some form of parental discretion. Few health care providers (18.6%) supported seeking explicit consent for screening condition-by-condition, but a larger minority (39.6%) supported seeking consent for the disclosure of incidentally generated sickle cell carrier results. Qualitative findings illuminate these preferences: respondents who favored consent emphasized its ease while dissenters saw consent as highly complex. CONCLUSION: Few providers supported explicit consent for NBS. Further, those who supported consent viewed it as a simple process. Arguably, these attitudes reflect the public health emergency NBS once was, rather than the public health service it has become. The complexity of NBS panels may have to be aligned with providers' capacity to implement screening appropriately, or providers will need sufficient resources to engage in a more nuanced approach to consent for expanded NBS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.070 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".