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Record W2111184769 · doi:10.1136/medethics-2011-100040

Proceduralisation, choice and parental reflections on decisions to accept newborn bloodspot screening

2011· article· en· W2111184769 on OpenAlexaff
Stuart G. Nicholls

Bibliographic record

VenueJournal of Medical Ethics · 2011
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPopulationPsychological interventionPresentation (obstetrics)Variety (cybernetics)Work (physics)MedicinePsychologyPublic relationsNursingEnvironmental healthPolitical scienceComputer scienceObstetricsEngineering

Abstract

fetched live from OpenAlex

Newborn screening is the programme through which newborn babies are screened for a variety of conditions shortly after birth. Programmes such as this are individually oriented but resemble traditional public health programmes because they are targeted at large groups of the population and they are offered as preventive interventions to a population considered healthy. As such, an ethical tension exists between the goals of promoting the high uptake of supposedly 'effective' population-oriented programmes and the goal of promoting genuinely informed decision-making. There is, however, a lack of understanding with regard to how parents experience the tension between promoting uptake and facilitating informed choice. This paper addresses this issue, and data are presented to show how aspects of the timing, presentation of information and procedural routinisation of newborn screening serves to impact on the decisions made by parents.

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 imitation

Not 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.

metaresearch head score (Codex)0.039
metaresearch head score (Gemma)0.141
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.204

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.141
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0050.017
Scholarly communication0.0080.005
Open science0.0010.005
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0020.000

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 head0.786
GPT teacher head0.647
Teacher spread0.139 · 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 source (direct Gemma or distilled Codex), 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

Citations31
Published2011
Admission routes1
Has abstractyes

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