Who might flourish and who might languish? Adolescent social and mental health profiles and their online experiences and behaviors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it