Examining the Effectiveness of Interactive Webtoons for Premature Birth Prevention: Protocol for a Randomized Controlled Trial
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,049 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,008 | 0,004 |
| Méta-épidémiologie (sens large) | 0,015 | 0,007 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,009 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,111 | 0,014 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».