Knowing Adolescents’ Social–Emotional Strengths
Bibliographic record
Abstract
Abstract: A growing body of research highlights the importance of positive social–emotional capacities, such as empathy and emotion regulation, in reducing mental health risks. A key challenge in school-based assessment is the lack of tools that reliably capture core social–emotional capacities with sensitivity and specificity for mental health outcomes. This paper explores the Social–Emotional Responding Task (SERT; Malti, 2017 ) in identifying needs (behavioral difficulties; low prosocial behavior) and strengths (high prosocial behavior) of 656 Chinese adolescents aged 11 – 17 ( M age = 13.73, SD = 1.16, 44 % female). Needs and strengths were assessed using the Strengths and Difficulties Questionnaire (SDQ; Goodman, 1997 ). Results showed that the SERT differentiated adolescents with prosocial strengths and needs in both self- and caregiver reports, with moderate accuracy for empathy subscales and lower accuracy for emotion regulation. For behavioral difficulties, some self-reports showed low but significant accuracy, whereas caregiver reports did not consistently differentiate needs. SERT may complement established screening instruments by particularly capturing social–emotional strengths.
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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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".