The Rise of Pregnancy Apps and the Implications for Culturally and Linguistically Diverse Women: Narrative Review
Notice bibliographique
Résumé
BACKGROUND: Pregnancy apps are a booming global industry, with most pregnant women in high-income countries now using them. From the perspective of health care and health information provision, this is both encouraging and unsettling; the demand indicates a clear direction for the development of future resources, but it also underscores the importance of processes ensuring access, reliability, and quality control. OBJECTIVE: This review provides an overview of current literature on pregnancy apps and aims at describing (1) the ways in which apps are used by women, in general, and by those of a culturally and linguistically diverse (CALD) background; (2) the utility and quality of information provided; and (3) areas where more research, development, and oversight are needed. METHODS: We chose a narrative review methodology for the study and performed a structured literature search including studies published between 2012 and 2017. Searches were performed using MEDLINE, EMBASE, and CINAHL databases. Studies were identified for inclusion using two separate search criteria and strategies: (1) studies on pregnancy apps and pregnant women's use of these apps and (2) studies on CALD pregnant women and their use of technology for accessing information on and services for pregnancy. Overall, we selected 38 studies. RESULTS: We found that pregnancy apps were principally used to access pregnancy health and fetal development information. Data storage capability, Web-based features or personalized tools, and social media features were also popular app features sought by women. Lower rates of the pregnancy app uptake were indicated among lower-income and non-English-speaking women. Preliminary evidence indicates that a combination of technological, health literacy, and language issues may result in lower uptake of pregnancy apps by these groups; however, further investigation is required. A marked limitation of the health app industry is lack of regulation in a commercially dominated field, making it difficult for users to assess the reliability of the information being presented. Health professionals and users alike indicate that given the choice, they would prefer using pregnancy apps that are relevant to their local health care context and come from a trusted source. Evidence indicates a need for greater health professional and institutional engagement in the app development, as well as awareness of and guidance for women's use of these resources. CONCLUSIONS: This is the first review of pregnancy app use, types of information provided, and features preferred by pregnant women in general and by those of a CALD background in particular. It indicates the demand for access to accurate information that is relevant to users, their community, and their associated health services. Given the popularity of pregnancy apps, such apps have enormous potential to be used for the provision of accurate, evidence-based health information.
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 | 0,005 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,007 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».