Women who are well informed about prenatal genetic screening delay emotional attachment to their fetus
Bibliographic record
Abstract
BACKGROUND: Prenatal maternal serum screening allows assessment of risk of chromosomal abnormalities in the fetus and is increasingly being offered to all women regardless of age or prior risk. However ensuring informed choice to participate in screening is difficult and the psychological implications of making an informed decision are uncertain. The aim of this study was to compare the growth of maternal-fetal emotional attachment in groups of women whose decisions about participation in screening were informed or not informed. METHODS: A prospective longitudinal design was used. English speaking women were recruited in antenatal clinics prior to the offer of second trimester maternal screening. Three self-report questionnaires completed over the course of pregnancy used validated measures of informed choice and maternal-fetal emotional attachment. Attachment scores throughout pregnancy in informed and not-informed groups were compared in repeated measures analysis. RESULTS: 134 completed the first assessment (recruitment 73%) and 68 (58%) provided compete data. The informed group had significantly lower attachment scores (p = 0.023) than the not-informed group prior to testing, but scores were similar (p = 0.482) after test results were known. CONCLUSION: The findings raise questions about the impact of delayed maternal-fetal attachment and appropriate interventions to facilitate informed choice to participate in screening.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".