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Record W2112270693 · doi:10.1177/1049732315589745

Women’s Experiences of Publicly Funded Non-Invasive Prenatal Testing in Ontario, Canada

2015· article· en· W2112270693 on OpenAlexafffundabout
Meredith Vanstone, Karima Yacoub, Mita Giacomini, Danielle Hulan, Sarah D. McDonald

Bibliographic record

VenueQualitative Health Research · 2015
Typearticle
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health ResearchPublic Health Agency of CanadaMcMaster University
KeywordsPrenatal screeningConstructivist grounded theoryTest (biology)Prenatal careGrounded theoryPsychologyFamily medicineMedicineNursingQualitative researchPregnancyEnvironmental healthPrenatal diagnosisSociology

Abstract

fetched live from OpenAlex

Non-invasive prenatal testing (NIPT) via fetal DNA in maternal blood has been publicly funded in Ontario, Canada, for high-risk women since 2014. We solicited women's experiences and values related to this new health technology to describe how this test is currently being used in Ontario and to provide information about patient priorities to inform future policy decisions about the use of NIPT. Guided by constructivist grounded theory methodology, we interviewed 38 women who had diverse personal experiences with NIPT. Participants' accounts of their values for decision making about NIPT heavily relied on three mutually modulating factors: timing, accuracy, and risk. The values expressed by women conflict with the way that publicly funded NIPT has typically been implemented in Ontario. We offer recommendations for how NIPT might be integrated into prenatal care pathways in a way more consistent with women's values.

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)0.004
metaresearch head score (Gemma)0.009
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 score0.123
Threshold uncertainty score0.894

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0220.010
Scholarly communication0.0050.002
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.506
GPT teacher head0.507
Teacher spread0.001 · 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

Citations49
Published2015
Admission routes3
Has abstractyes

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