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Record W4392744832 · doi:10.32920/25403623.v1

Are Midwifery Clients in Ontario Making Informed Choices About Prenatal Screening?

2024· preprint· en· W4392744832 on OpenAlexaboutno aff
Nadya Burton, Vanessa Dixon

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsObstetricsMedicineNursingPsychology

Abstract

fetched live from OpenAlex

Background: Informed choice is often lacking in women's decisions about prenatal screening. Aim: The aim of this study is to evaluate how well midwives in Ontario, Canada are facilitating informed choice in this area. Methods: An Internet-based survey was used to investigate 171 midwifery clients' knowledge, attitude towards and experience of prenatal genetic screening tests, and to determine the proportion of study participants who made an informed choice about prenatal screening. Findings: All participants demonstrated adequate knowledge of prenatal screening. The vast majority (93.0%) of participants made an informed choice. Participants who chose to screen had lower knowledge scores than those who opted out of screening. Client satisfaction rates in regard to care received in this area ranged from 97% to 100%. Conclusions: Results of this study suggest that Ontario midwives are effective in conveying information on prenatal genetic screening, contributing to high levels of client knowledge and satisfaction in comparison to similar studies in other jurisdictions.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0060.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.047
GPT teacher head0.322
Teacher spread0.274 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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