I thought we were good: social cognition, figurative language, and adolescent psychopathology
Bibliographic record
Abstract
BACKGROUND: Language has been shown to play a critical role in social cognitive reasoning in preschool and school-aged children, but little research has been conducted with adolescents. During adolescence, the ability to understand figurative language becomes increasingly important for social relationships and may affect social adjustment. This study investigated the contribution of structural and figurative language to social cognitive skills in adolescents who present for mental health services and those who do not. METHOD: One hundred and thirty-eight adolescents referred to mental health centers (clinic group) and 186 nonreferred adolescents (nonclinic group) aged 12-17 were administered measures of structural and figurative language, working memory, and social cognitive problem solving. RESULTS: We found that adolescents in the clinic group demonstrated less mature social problem solving overall, but particularly with respect to anticipating and overcoming potential obstacles and conflict resolution compared with the nonclinic group. In addition, results demonstrated that age, working memory, and structural and figurative language predicted social cognitive maturity in the clinic group, but only structural language was a predictor in the nonclinic group. CONCLUSIONS: Social problem solving may be particularly difficult for adolescents referred for mental health services and places higher demands on their cognitive and language skills compared with adolescents who have never been referred for mental health services.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".