Ranking Adolescent Stressors Linked to Urban Lifestyle and Overexposure
Bibliographic record
Abstract
Objective: This study aimed to identify and rank key stressors affecting adolescents in urban environments, focusing on how digital exposure, academic competition, environmental overload, and social comparison contribute to mental and emotional distress. Methods and Materials: The research employed a sequential exploratory mixed-methods design. In the qualitative phase, an extensive literature review was conducted using NVivo 14 software to identify major categories of urban stressors until theoretical saturation was achieved. Eight primary themes emerged—academic pressure, digital overexposure, social comparison, lifestyle imbalance, sensory overload, media-driven anxiety, urban isolation, and spatial constraints. In the quantitative phase, a structured questionnaire derived from these themes was distributed to 200 adolescents (aged 13–18) from urban regions of Hungary. Data were analyzed using SPSS version 26, with descriptive statistics, the Friedman test, and reliability analysis (Cronbach’s α = 0.89) applied to determine the relative significance of stressors. Findings: The Friedman test results indicated statistically significant differences in the mean ranks of stressors (p < 0.001). Academic and performance pressure ranked highest (mean rank = 4.67), followed by digital overexposure (4.51), social comparison (4.38), and lifestyle imbalance (4.21). Environmental and media-related stressors—such as sensory overload and media-driven anxiety—were moderately ranked, while urban isolation and physical constraints had the lowest rankings. These findings suggest that cognitive and social pressures related to performance and technology use are stronger predictors of adolescent distress than physical or environmental factors. Conclusion: The study demonstrates that the urban adolescent experience is characterized by overlapping academic and digital stressors that operate within a high-demand environment. Understanding the hierarchical structure of these stressors can inform interventions promoting balance, digital mindfulness, and academic reform to mitigate chronic urban stress among youth.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".