Decisional needs assessment regarding Down syndrome prenatal testing: a systematic review of the perceptions of women, their partners and health professionals
Bibliographic record
Abstract
OBJECTIVE: To identify decisional needs of women, their partners and health professionals regarding prenatal testing for Down syndrome through a systematic review. METHODS: Articles reporting original data from real clinical situations on sources of difficulty and/or ease in making decisions regarding prenatal testing for Down syndrome were selected. Data were extracted using a taxonomy adapted from the Ottawa Decision-Support Framework and the quality of the studies was assessed using Qualsyst validated tools. RESULTS: In all 40 publications covering 32 unique studies were included. The majority concerned women. The most often reported sources of difficulty for decision-making in women were pressure from others, emotions and lack of information; in partners, emotion; in health professionals, lack of information, length of consultation, and personal values. The most important sources of ease were, in women, personal values, understanding and confidence in the medical system; in partners, personal values, information from external sources, and income; in health professionals, peer support and scientific meetings. CONCLUSION: Interventions regarding a decision about prenatal testing for Down syndrome should address many decisional needs, which may indeed vary among the parties involved, whether women, their partners or health professionals. Very little is known about the decisional needs of partners and health professionals.
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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 | 0.014 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".