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Record W1988522070 · doi:10.1007/s10897-014-9780-x

Education and Parental Involvement in Decision‐Making About Newborn Screening: Understanding Goals to Clarify Content

2014· article· en· W1988522070 on OpenAlexaff
Beth K. Potter, Holly Etchegary, Stuart G. Nicholls, Brenda J. Wilson, Samantha Craigie, Makda H. Araia

Bibliographic record

VenueJournal of Genetic Counseling · 2014
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsMemorial University of NewfoundlandUniversity of Ottawa
Fundersnot available
KeywordsContent analysisPsychologyContent (measure theory)SociologySocial science

Abstract

fetched live from OpenAlex

A challenge in designing effective education for parents about newborn screening (NBS) has been uncertainty about appropriate content. Arguing that the goals of education may be usefully tied to parental decision-making, we sought to: (1) explore how different ways of implementing NBS differ in their approaches to parental engagement in decision-making; (2) map the potential goals of education onto these "implementation models"; and (3) consider the content that may be needed to support these goals. The resulting conceptual framework supports the availability of comprehensive information about NBS for parents, irrespective of the model of implementation. This is largely because we argue that meeting parental expectations and preferences for communication is an important goal regardless of whether or notparents are actively involved in making a decision. Our analysis supports a flexible approach, in which some educational messages are emphasized as important for all parents to understand while others are made available depending on parents' preferences. We have begun to define the content of NBS education for parents needed to support specific goals. Further research and discussion is important to determine the most appropriate strategies for delivering the tailored approach to education that emerged from our analysis.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.057
GPT teacher head0.351
Teacher spread0.294 · 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 designObservational
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

Citations12
Published2014
Admission routes1
Has abstractyes

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