Social Media and Mental Health in Adolescents
Bibliographic record
Abstract
This study employs a quantitative research approach to investigate the relationship between social media use and adolescent mental health. The evolution of social media has revolutionized communication, becoming an integral part of daily life. Numerous studies have shown that adolescents (ages 12-19) spend significant time on social media platforms, impacting mental health. (Kaur et al., 2022) in India. In the case of Nepal, adolescent students spend a good amount of time on social media. However, the impact of using social media by adolescent students is not systematically investigated. (Kharel, 2023) This research study aims to examine the relationship between social media use and adolescent mental health. It has conducted a structured survey with 260 participants from Kathmandu Valley and Dang Valley in Nepal. It has defined various factors as well-being, psychological, risk, value, and perceived factors. Responses were recorded on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Cronbach’s alpha test (0.94) confirms strong internal consistency. While conducting the sampling, a 95% confidence level was assumed, with a desired margin of error set at 5% and an expected population proportion of 0.2. Results indicate no significant gender differences (ANOVA p = 0.56), but linear regression analysis reveals a mental health outcome of 12.5. Cronbach’s alpha test (0.94) confirms strong internal consistency. The findings indicate no significant gender differences but emphasize the need for targeted interventions to mitigate social media's negative impact on adolescent mental health. Mental health professionals should focus on early detection, its impact, and preventive strategies to support adolescent mental health. Keywords: Social media impact, adolescent mental health, social media and mental health, positive and negative impact of social media, mental health survey
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".