MétaCan
Menu
Retour à la cohorte
Enregistrement W4415788382 · doi:10.70818/pjmr.2024.v01i01.042

Parents Perception of Using Digital Technology Among Preschool Children in Selected Schools in an Urban Community

2024· article· W4415788382 sur OpenAlexaff
Susmita Deb Nath, Bijoy Kumer Paul, Syed Shariful Islam, Shaikh Kaniz Sayeda, Songeeta Sarker

Notice bibliographique

RevuePacific Journal of Medical Research · 2024
Typearticle
Langue
DomaineSocial Sciences
ThématiqueChild Development and Digital Technology
Établissements canadiensPrivy Council Office
Organismes subventionnairesnon disponible
Mots-clésPerceptionPoint (geometry)Face (sociological concept)Data collectionFace-to-faceUrban community

Résumé

récupéré en direct d'OpenAlex

Background: In the world we live in now, technological devices are becoming more and more important. Digital technology can be a source of information and a good way to learn new things, but it also has some draw backs. Devices like tablets, phones, and computers have taken the place of toys that children used to like. When looked at from this point of view, it can be said that many common technological devices today have both good and bad effects on people. Objective: To assess the parent’s perception of digital technology usage of preschool children in selected schools in Dhaka city, Bangladesh. Methodology: The cross-sectional study was carried out to determine the A total of 123 preschool children’s (age:3-6yrs) parents participated in the study, parents’ perception of digital technology usage of preschool children in selected schools (YWCA Higher Secondary Girls School, Assemblies of GOD Church School, Silver dale Preparatory Girls High School and Zamzam Point Int. School & College) in Dhaka city, Bangladesh from January 2024 to November 2024. Data were collected by face to face interview with the parents by semi structured questionnaire. The participants were selected by convenient sampling procedure. Ethical permission was obtained from the Institutional Review Board(IRB) of BSMMU. Results: A total of 123 parents were participated in the study. Among them, 83.7% were women and 16.3% were men.60.2% of the mothers were between the ages of 20 and 30, and 78% of the fathers were between the ages of 20 and 30. 26.8% of mothers had completed H.S.C level, and 31.7% of fathers had a master's degree or more. Among the parents,48.8% Parents thought that children first used digital technology between the ages of 3 and 4 years. According to Parents perception, half of their children used digital technology for two hours a day. 69% children used digital technology for social media. According to Parents perception 59.3% of their children were generally well-behaved and usually did what adults asked, but 13.8% children often fight with other children. Among them, 36.6% of children had headaches,35.5% had body aches, 13.8% had decreased visual activity, 17.9% had lost weight and felt tired, and 40.7% were lack of sleep disruption. As a result, the children also experienced some psychological effects. 64.2% Parents thought that their children overused digital technology, which took away from their study time, 63.4% of children made them less creative, and 49.6% of children were a little bit restless. Conclusion: Digital technology use can influence a child's physical, psychological and social health. Parents influence children positive usage of technology. In order for children to adopt a healthy lifestyle, it is essential to monitor the amount of time, frequency, and content viewed while using technological devices and to ensure that children have or develop adequate opportunities for physical activity, healthy eating habits, proper sleep cycles, and supportive social relationships. Awareness program should be conducted about proper use of digital technology. Further studies involving larger sample size and addressing geographical variations are needed for generalizability.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,016
Score d'incertitude au seuil0,032

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,065
Tête enseignante GPT0,412
Écart entre enseignants0,347 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revuePacific Journal of Medical ResearchMême sujetChild Development and Digital TechnologyTravaux en français237 207