Information Resources Among Flemish Pregnant Women: Cross-sectional Study
Notice bibliographique
Résumé
BACKGROUND: There has been an exponential growth in the availability of apps, resulting in increased use of pregnancy apps. However, information on resources and use of apps among pregnant women is relatively limited. OBJECTIVE: The aim of this study is to map the current information resources and the use of pregnancy apps among pregnant women in Flanders. METHODS: A cross-sectional study was conducted, using a semistructured survey (April-June 2019) consisting of four different domains: (1) demographics; (2) use of devices; (3) sources of information; and (4) use of pregnancy apps. Women were recruited by social media, flyers, and paper questionnaires at prenatal consultations. Statistical analysis was mainly focused on descriptive statistics. Differences in continuous and categorical variables were tested using independent Student t tests and chi-square tests. Correlations were investigated between maternal characteristics and the women's responses. RESULTS: In total, 311 women completed the entire questionnaire. Obstetricians were the primary source of information (268/311, 86.2%) for pregnant women, followed by websites/internet (267/311, 85.9%) and apps (233/311, 74.9%). The information that was most searched for was information about the development of the baby (275/311, 88.5%), discomfort/complaints (251/311, 80.7%) and health during pregnancy (248/311, 79.7%), administrative/practical issues (233/311, 74.9%), and breastfeeding (176/311, 56.6%). About half of the women (172/311, 55.3%) downloaded a pregnancy app, and primarily searched app stores (133/311, 43.0%). Pregnant women who are single asked their mothers (22/30, 73.3%) or other family members (13/30, 43.3%) for significantly more information than did married women (mother [in law]: 82/160, 51.3%, P=.02; family members: 35/160, 21.9%, P=.01). Pregnant women with lower education were significantly more likely to have a PC or laptop than those with higher education (72/73, 98.6% vs 203/237, 85.5%; P=.008), and to consult other family members for pregnancy information (30/73, 41.1% vs 55/237, 23.1%; P<.001), but were less likely to consult a gynecologist (70/73, 95.9% vs 198/237, 83.5%; P=.001). They also followed more prenatal sessions (59/73, 80.8% vs 77/237, 32.5%; P=.04) and were more likely to search for information regarding discomfort/complaints during pregnancy (65/73, 89% vs 188/237, 79.5%; P=.02). Compared to multigravida, primigravida were more likely to solicit advice about their pregnancy from other women in their social networks (family members: primigravida 44/109, 40.4% vs multigravida 40/199, 20.1%; P<.001; other pregnant women: primigravida 58/109, 53.2% vs multigravida 80/199, 40.2%; P<.03). CONCLUSIONS: Health care professionals need to be aware that apps are important and are a growing source of information for pregnant women. Concerns rise about the quality and safety of those apps, as only a limited number of apps are subjected to an external quality check. Therefore, it is important that health care providers refer to high-quality digital resources and take the opportunity to discuss digital information with pregnant women.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».