Informed Choice for Participation in Down Syndrome Screening: Development and Content of a Web-Based Decision Aid
Bibliographic record
Abstract
BACKGROUND: In Denmark, all pregnant women are offered screening in early pregnancy to estimate the risk of having a fetus with Down syndrome. Pregnant women participating in the screening program should be provided with information and support to allow them to make an informed choice. There is increasing interest in the use of Web-based technology to provide information and digital solutions for the delivery of health care. OBJECTIVE: The aim of this study was to develop an eHealth tool that contained accurate and relevant information to allow pregnant women to make an informed choice about whether to accept or reject participation in screening for Down syndrome. METHODS: The development of the eHealth tool involved the cooperation of researchers, technology experts, clinicians, and users. The underlying theoretical framework was based on participatory design, the International Patient Decision Aid Standards (IPDAS) Collaboration guide to develop a patient decision aid, and the roadmap for developing eHealth technologies from the Center for eHealth Research and Disease Management (CeHRes). The methods employed were a systematic literature search, focus group interviews with 3 care providers and 14 pregnant women, and 2 weeks of field observations. A qualitative descriptive approach was used in this study. RESULTS: Relevant themes from pregnant women and care providers with respect to information about Down syndrome screening were identified. Based on formalized processes for developing patient decision aids and eHealth technologies, an interactive website containing information about Down syndrome, methods of screening, and consequences of the test was developed. The intervention was based on user requests and needs, and reflected the current hospital practice and national guidelines. CONCLUSIONS: This paper describes the development and content of an interactive website to support pregnant women in making informed choices about Down syndrome screening. To develop the website, we used a well-structured process based on scientific evidence and involved pregnant women, care providers, and technology experts as stakeholders. To our knowledge, there has been no research on the combination of IPDAS standards and the CeHRes roadmap to develop an eHealth tool to target information about screening for Down syndrome.
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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 | 0.024 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".