Adolescents’ Perceptions of Social Media’s Impact on Mental Health and Well-Being During their Transition to University
Bibliographic record
Abstract
Adolescence is a crucial stage for developing good social connections and emotional habits within a supportive environment that promotes mental health and well-being. Online digital connection provides opportunities for adolescents to socialize; however, research (e.g., Abi-Jaoude et al., 2020; Kerr & Kingsbury, 2023; Statistics Canada, 2022) has also indicated that it negatively impacted adolescent mental health. However, it is relatively unknown how social media use and mental health simultaneously affect adolescents’ transition from high school to university. In conducting this study, opinions regarding social media use and the impact it has on adolescent mental health and well-being during the transition to university will be presented and interpreted. Using Social Influence Theory (SIT) and affordance theory lenses, semi-structured interviews were conducted with first- and second-year university students. The data was analyzed using reflexive thematic analysis and five overarching themes were conceptualized: 1) Knowledge and Understanding; 2) Changes in Social Media Use; 3) Coping During Difficult Times; 4) Mental Health and Well-Being During the Transition to University; and 5) Problematic Social Media Use. Unique individual findings were also discussed. These themes encompassed the unique lived experiences that participants had using social media and how it impacted their mental health and well-being during their transition into university.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".