The more, the better? Social capital profiles and adolescent internalizing symptoms: A latent profile analysis
Bibliographic record
Abstract
Abstract Past research suggests that offline and online social capital are empirically linked to adolescent psychological adjustment. However, little is known regarding the implications of distinctive combinations of social capital for adolescent internalizing symptoms. The present study aimed to examine adolescent social capital patterns and their associations with internalizing symptoms by using Latent profile analysis. A cross-sectional web-based survey was conducted among 1595 Chinese adolescents (mean age = 14.30 years, 50.7% male). All adolescents completed self-report questionnaires on their perceived offline and online social capital, depressive symptoms and anxiety symptoms. Latent profile analysis revealed four profiles of social capital: 1) Low Social Capital, 2) Moderate Social Capital, 3) High Social Capital, and 4) Only High Offline Social Capital. Further, analysis of covariance demonstrated that the Only High Offline Social Capital profile had significantly fewer internalizing symptoms than other three profiles. No statistical differences of internalizing symptoms were found between the other three profiles, except for the difference in anxiety symptoms between the Moderate Social Capital profile and the Low Social Capital profile. These findings suggest that the more social capital does not equal to the better mental health status. The social capital profiles and their associations with adolescent internalizing symptoms may provide practitioners with meaningful implications regarding the role of offline and online social capital in adolescent psychological adjustment.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".