A Cross-sectional Study on Purpose of Smartphone use among Urban Girls’ School Students
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".