Perceptions of genomic newborn screening: a cross-sectional survey conducted with UK medical students
Bibliographic record
Abstract
BACKGROUND: With the potential to identify a vast number of rare diseases soon after birth, genomic newborn screening (gNBS) could facilitate earlier interventions and improve health outcomes. Designing a gNBS programme will involve balancing stakeholders' opinions and addressing concerns. The views of medical students-future clinicians who would deliver gNBS-have not yet been explored. METHODS: We conducted a nationwide online survey of UK medical students via the REDCap platform. Perceptions of gNBS, including scope of testing and potential benefits and drawbacks, were explored using a mix of multiple-choice questions, Likert scales, visual analogue scales and free-text questions. RESULTS: In total, 116 medical students across 16 universities participated. Overall, 45% supported gNBS, with a positively skewed mean support score of 3.24 (SD 1.26, range: 1.0-5.0), and 55% felt it relevant to their future practice. Almost all agreed that infant-onset and childhood-onset diseases and conditions with effective treatments should be included. Most felt that earlier interventions and personalised care would be the most important benefit of gNBS. Other perceived benefits included earlier diagnoses, diagnosing more patients and enabling research for new treatments. However, several perceived challenges were highlighted: risk of genomic discrimination, incidental or uncertain findings, data security and breaching children's future autonomy. Students expressed conflicting opinions on the psychological impact on families, but most were concerned about a lack of support due to current resource limitations in health services. Students frequently reported having insufficient knowledge to form an opinion, which may reflect gaps in genomics education at medical school and the current lack of evidence base for gNBS. CONCLUSION: Although some support for gNBS was demonstrated, ethicolegal and social challenges were raised, emphasising a need for ongoing discussions about the implications of gNBS.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".