Examining the Effectiveness of Interactive Webtoons for Premature Birth Prevention: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Premature birth poses significant health challenges globally, impacting infants, families, and society. Despite recognition of its contributing factors, efforts to reduce its incidence have seen limited success. A notable gap exists in the awareness among women of childbearing age (WCA) regarding both the risks of premature birth and the preventative measures they can take. Research suggests that enhancing health beliefs and self-management efficacy in WCA could foster preventive health behaviors. Interactive webtoons offer an innovative, cost-effective avenue for delivering engaging, accessible health education aimed at preventing premature birth. OBJECTIVE: This protocol describes a randomized controlled trial to assess the effectiveness and feasibility of a novel, self-guided, web-based intervention-Pregnancy Story I Didn't Know in Interactive Webtoon Series (PSIDK-iWebtoons)-designed to enhance self-management efficacy and promote behaviors preventing premature birth in WCA. METHODS: Using an explanatory sequential mixed methods design, this study first conducts a quantitative analysis followed by a qualitative inquiry to evaluate outcomes and feasibility. Participants are randomly assigned to 2 groups: one accessing the PSIDK-iWebtoons and the other receiving Pregnancy Story I Didn't Know in Text-Based Information (PSIDK-Texts) over 3 weeks. We measure primary efficacy through the self-management self-efficacy scale for premature birth prevention (PBP), alongside secondary outcomes including perceptions of susceptibility, severity, benefits, and barriers based on the health belief model for PBP and PBP intention. Additional participant-reported outcomes are assessed at baseline, the postintervention time point, and the 4-week follow-up. The feasibility of the intervention is assessed after the end of the 3-week intervention period. Outcome analysis uses repeated measures ANOVA for quantitative data, while qualitative data are explored through content analysis of interviews with 30 participants. RESULTS: The study received funding in June 2021 and institutional review board approval in October 2023. Both the PSIDK-iWebtoons and PSIDK-Texts interventions have been developed and pilot-tested from July to November 2023, with the main phase of quantitative data collection running from November 2023 to March 2024. Qualitative data collection commenced in February 2024 and will conclude in May 2024. Ongoing analyses include process evaluation and data interpretation. CONCLUSIONS: This trial will lay foundational insights into the nexus of interactive web-based interventions and the improvement of knowledge and practices related to PBP among WCA. By demonstrating the efficacy and feasibility of a web-based, interactive educational tool, this study will contribute essential evidence to the discourse on accessible and scientifically robust digital platforms. Positive findings will underscore the importance of such interventions in fostering preventive health behaviors, thereby supporting community-wide efforts to mitigate the risk of premature births through informed self-management practices. TRIAL REGISTRATION: Korea Disease Control and Prevention Agency (KDCA) KCT0008931; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=25857. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58326.
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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 | 0.049 | 0.048 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.111 | 0.014 |
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".