Who might flourish and who might languish? Adolescent social and mental health profiles and their online experiences and behaviors
Bibliographic record
Abstract
Extant research has identified associations between social media and internet use on the social and mental wellbeing of adolescents. While associations are clear, less apparent is why some adolescents respond and behave differently to the risks and benefits associated with social media use. Specifically, a paucity of work has examined how mental (i.e. anxiety and depression) and social (i.e. peer acceptance) factors work together to impact technology use, behaviors, and experiences. Thus, the purpose of this study was to (a) use latent profile analysis (LPA) to identify adaptive and maladaptive adolescent social and mental health profiles and (b) examine the links between these profiles and demographic variables, time spent online, reasons for going online, privacy-/oversharing-related behaviors, and cyberbullying and victimization instances. Among a sample of grades 6 and 7 students (n = 671), we examined students' reports of social acceptance, depression, and anxiety. Using LPA, we identified three profiles of social and mental health: a flourishing profile (high social acceptance, low depression, and low anxiety), a moderate profile (average social acceptance, above average depression, above average anxiety), and a languishing profile (low social acceptance, high depression, and high anxiety). Findings showed significant differences across the profiles in relation to several sociodemographic factors and online behaviors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".