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Record W2005959110 · doi:10.1111/hex.12047

Expectations and values about expanded newborn screening: a public engagement study

2013· article· en· W2005959110 on OpenAlexafffundabout
Robin Z. Hayeems, Fiona A. Miller, Yvonne Bombard, Denise Avard, June Carroll, Brenda J. Wilson, Julian Little, Pranesh Chakraborty, Jessica Bytautas, Yves Giguère, Judith Allanson, Renata Axler

Bibliographic record

VenueHealth Expectations · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsUniversité LavalMount Sinai HospitalChildren's Hospital of Eastern OntarioMcGill UniversityUniversity of OttawaMcGill Genome CentreNewborn Screening OntarioUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsAnxietyNewborn screeningStigma (botany)Context (archaeology)EnthusiasmMedicineFocus groupFamily medicinePsychologyPopulationInformed consentClinical psychologyPsychiatryAlternative medicineSocial psychologyPediatricsBusinessEnvironmental healthMarketing

Abstract

fetched live from OpenAlex

OBJECTIVES: Newborn bloodspot screening (NBS) panels have expanded to include conditions for which treatment effects are less certain, creating debate about population-based screening criteria. We investigated Canadian public expectations and values regarding the types of conditions that should be included in NBS and whether parents should provide consent. METHODS: Eight focus groups (FG; n = 60) included education, deliberative discussion and pre-/post-questionnaires. Data were analysed quantitatively and qualitatively. RESULTS: Quantitatively, the majority supported NBS for serious disorders for which treatment is not available (95-98, 82%). A majority endorsed screening without explicit consent (77-88%) for treatable disorders, but 62% supported unpressured choice for screening for untreatable disorders. Qualitatively, participants valued treatment-related benefits for infants and informational benefits for families. Concern for anxiety, stigma and unwanted knowledge depended upon disease context and strength of countervailing benefits. CONCLUSIONS: Anticipated benefits of expanded infant screening were prioritized over harms, with information provision perceived as a mechanism for mitigating harms and enabling choice. However, we urge caution around the potential for public enthusiasm to foster unlimited uptake of infant screening technologies.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.661

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.327
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2013
Admission routes3
Has abstractyes

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