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Record W7051565603

Parental consent for newborn screening:
\na discrete choice experiment

2021· dissertation· en· W7051565603 on OpenAlexaboutno aff

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2021
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionGestational periodArticular cartilage damageTSG101HyporeflexiaDiafiltration
DOInot available

Abstract

fetched live from OpenAlex

Background: Parental consent is very commonly assumed for newborn bloodspot screening (NBS)
\nin most Canadian provincial screening programs. This falls short of usual norms, and evidence
\nsuggests that some parents would prefer an explicit process. This study was designed to inform
\nimprovements in NBS consent processes.
\nObjectives: (1) To examine parents’ past experiences with, and attitudes towards, NBS consent
\nprocesses in Canada. (2) To quantify parents’ preferences towards specific attributes of the NBS
\nconsent process, and identify characteristics of subgroups with different preference patterns.
\nMethod: A cross-sectional survey that included a discrete choice experiment (DCE) was
\nconducted to capture information on participants’ past experiences with and preferences for NBS
\nconsent processes. DCE data were analyzed using conditional logit and latent class (LC) regression
\nmodels.
\nResults: The sample comprised 715 participants. As an overall group, respondents preferred to
\nhave NBS information provided late in pregnancy, for consent not to be assumed by providers, and
\nfor the consent decision to always be recorded. Three classes of participants with different
\nunderlying preference patterns were identified in the sample.
\nConclusion: If NBS programs wish to better meet parents’ preferenes, the results indicate specific
\naspects of the consent process that could be targeted for further examination..

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.234
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.306
Teacher spread0.272 · 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.

Study designNot applicable
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

Citations0
Published2021
Admission routes1
Has abstractyes

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