Development of a Tailored Online Video-Based Assistant to Support Prenatal Screening Decisions in Couples With Limited Health Literacy: User-Centered Design Approach
Bibliographic record
Abstract
BACKGROUND: Up to 25% of pregnant couples in the Netherlands do not make an informed decision about prenatal screening: their decisions are value-inconsistent or based on insufficient knowledge and deliberation. More than one-third (36%) of the population in the Netherlands has limited health literacy skills, with the majority being individuals with lower levels of education or a migration background. They experience serious problems in understanding health information and taking an active role in decision-making. Therefore, the Dutch Health Council recommends improving decision support for pregnant couples. OBJECTIVE: This study aimed to describe the rationale and systematic design of an online, interactive, and tailored video-based assistant to support pregnant couples with limited health literacy skills in decision-making on prenatal screening. METHODS: The intervention mapping framework was used for the iterative user-centered development of the decision aid prenatal screening. This includes the following steps: (1) a needs assessment among the target group (ie, pregnant couples and counselors), (2) defining change objectives based on the needs assessment, (3) selection of theoretical methods, (4) program production and prototype testing, (5) implementation planning, and (6) preparation for evaluation. Three prototypes of the decision aid were iteratively tested among pregnant couples (with low literacy), counselors, and stakeholders relevant for future implementation. This paper describes steps 1 to 4 of the decision aid development. RESULTS: We developed a decision aid guided by a virtual assistant to promote informed decision-making among pregnant couples (with low health literacy) on prenatal screening. To comply with users' needs, it includes the following four interactive modules: (1) Other people's experiences, (2) Information about the tests (and anomalies), (3) Help me decide, and (4) Further questions. To increase accessibility, it features a menu that allows for adapting the speed of speech, making use of subtitles, and is offered in 3 different languages. To increase implementation and future use of the decision aid, an e-learning tool was developed. The decision aid can be used either as a stand-alone tool by pregnant couples or in combination with a counselor during counseling sessions. CONCLUSIONS: By describing the systematic development of a prenatal screening decision aid designed to support pregnant couples with low health literacy in making well-informed choices, we aimed to contribute to systematic reporting and transparent intervention design. TRIAL REGISTRATION: International Standard Randomised Controlled Trial Registry ISRCTN18016226; https://www.isrctn.com/ISRCTN18016226.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".