Pregnant women's intention to use a mobile application-based decision aid for prenatal screening for trisomies 21, 18 and 13
Bibliographic record
Abstract
Context: Decision-making regarding prenatal screening is complex that can be supported by from decision aids. Mobile health technologies have led to apps that assist pregnant women in making informed health decisions. Objective: assess pregnant women9s intention to use a mobile application to make decisions about prenatal screening for trisomies 21, 18 and 13. Study Design and Analysis: Mixed-methods cross-sectional study complying with STROBE and COREQ guidelines, including descriptive, bivariate and multivariate analyses of quantitative data and thematic analysis of qualitative data. Setting: Study conducted in Quebec City and Montreal, among women at least 16 weeks pregnant or who had given birth in the previous year, and who had no high-risk pregnancies. Population Studied: Participants included 67 eligible pregnant women, mostly Canadian, French-speaking, aged 25 to 34 and highly educated. Instrument: For the quantitative phase, participants used a paper-based decision aid about prenatal screening and completed a questionnaire, the CDP-Reaction, to assess their intention as well as psychosocial determinants related to the intention to use a mobile app with similar content. For the qualitative phase, participants viewed a video on shared decision-making using the paper-based medium described above and discussed their use of smartphones as well as mobile health apps. Outcome Measures: The primary outcome measured was pregnant women9s intention to use the mobile application, quantified on a scale of 1 to 7, and then the identification of these potential predictors. Results: The mean intention score was 4.92 out of 7, indicating a strong intention to use the mobile app. The significant factors positively associated with this intention were beliefs in consequences (β: 1.21; 5% CI: 1.02 – 1.39; p <0.0001) and social influence (β: 0.17; 5% CI: 0.01-0.32 ; p=0.03). Most of the women who had already used pregnancy apps rated the mobile app positively, but also many were open to adopting other formats of such a decision aid (web version, paper etc.). Conclusions: The results suggest a strong intention among pregnant women to use the mobile app to make the decision in relation to screening for trisomies 21, 18 and 13, influenced by perceived advantages and disadvantages of its use and peer opinion. Interventions to promote informed choice in prenatal screening should target these influencing factors.
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 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".