A Cross-sectional Study on Purpose of Smartphone use among Urban Girls’ School Students
Notice bibliographique
Résumé
Background: Smartphones have become an integral part of students’ daily lives, particularly among secondary school students. With easy access to the internet, social media, and various applications, smartphones offer powerful tools for communication, learning, and entertainment. Excessive smartphone use among school children therefore raises concern about academic performances, sleep pattern and mental health. As a result the balance between educational benefits and negative impacts of smartphone use has become an area of concern to study. Methods: This descriptive cross-sectional study was conducted from 6th to 20th March 2023. An urban girls’ school was selected and data were collected from the students of class VIII, class IX and class X using purposive sampling technique. A semi-structured questionnaire was used to collect data and the final sample size was 275. The data were analyzed via SPSS (version 25.0). Results: The mean ± SD (Standard Deviation) age of the girls was 14.55±0.99 years. The majority of the students were in class VIII (49.8%) and Muslim (87.2%). More than one-fourth of the students’ fathers were graduates (26.6%) and mothers were educated up to SSC (27.6%). The majority of the students’ fathers were involved in business (33.8%) and mothers were housewives (85.5%). More than four-fifths (85.5%) of the students represented nuclear families and two-fifths (43.3%) had a monthly family income of below Tk. 30,000. The majority (44%) first started using a smartphone from class V or VI. Nearly half of them (42.1%) used their mothers’ phone. Majority used android phones (78.2%), anytime (51.6%) during holidays, after school (86.9%), 1 to 2 hours (41.5%) a day. Only one-tenth of the students (10.5%) usually brought their smartphones to school depending on the situation. The majority of the students used smartphones for knowledge and education (77.5%), social media (54.2%), communication with friends and relatives (52%) purposes and frequently used YouTube (74.2%), WhatsApp (65.8%) and Instagram (35.6%) as social media sites. Conclusion: Mobile phone has a significant influence on the young generation which leads to create a networked society. This study was conducted in order to better interpret the usage pattern of mobile phone among secondary school students. Northern International Medical College Journal Vol. 15 No. 1-2 July 2023-January 2024, Page 667-671
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,004 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 tête enseignante, 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 ».