Experiences, Attitudes, and Needs of Users of a Pregnancy and Parenting App (Baby Buddy) During the COVID-19 Pandemic: Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic has impacted the lives of expectant parents and parents of young babies, with disruptions in health care provision and loss of social support. OBJECTIVE: This study investigated the impact of the COVID-19 pandemic and its associated lockdown on this population through the lens of users of the UK National Health Service-approved pregnancy and parenting smartphone app, Baby Buddy. The study aims were threefold: to gain insights into the attitudes and experiences of expectant and recent parents (with babies under 24 weeks of age) during the COVID-19 pandemic; to investigate whether Baby Buddy is meeting users' needs during this time; and to identify ways to revise the content of Baby Buddy to better support its users now and in future. METHODS: A mixed methods study design combining a web-based survey with semistructured telephone interviews among Baby Buddy users in the United Kingdom was applied. Data were collected from April 15 to mid-June 2020, corresponding to weeks 4-13 of the lockdown in the United Kingdom. RESULTS: A total of 436 expectant (n=244, 56.0%) and recent (n=192, 44.0%) parents responded to the web-based survey, of which 79.1% (n=345) were aged 25-39 years and 17.2% (n=75) spoke English as their second language. Of the 436 respondents, 88.5% (386/436) reported increased levels of anxiety around pregnancy, birth, and being a new parent, and 58.0% (253/436) were concerned about their emotional and mental health. Of the 244 pregnant respondents, 43.4% (n=106) were concerned about their physical health. Telephone interviews with 13 pregnant women and 19 recent parents revealed similarly increased levels of anxiety due to reduced health care provision and loss of support from friends and family. Although a minority of respondents identified some positive outcomes of lockdown, such as family bonding, many telephone interviewees reported feeling isolated, disregarded, and overwhelmed. Recent parents were particularly anxious about the impact of the lockdown on their baby's development and socialization. Many interviewees were also concerned about their physical health as a consequence of both limited access to face-to-face medical appointments and their own poorer dietary and physical activity behaviors. Across both samples, 97.0% (423/436) of respondents reported that Baby Buddy was currently helping them, with many commenting that its role was even more important given the lack of face-to-face support from health care and parenting organizations. Greater speed in updating digital content to reflect changes due to the pandemic was suggested. CONCLUSIONS: The COVID-19 pandemic has created heightened anxiety and stress among expectant parents and those with a young baby, and for many, lockdown has had an adverse impact on their physical and mental well-being. With reductions in health care and social support, expectant and new parents are increasingly relying on web-based resources. As a free, evidence-based app, Baby Buddy is well positioned to meet this need. The app could support its users even more by actively directing them to the wealth of existing content relevant to their concerns and by adding content to give users the knowledge and confidence to meet new challenges.
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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 | 0,004 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».