Applying machine learning to understand the role of social–emotional skills on subjective well‐being and physical health
Bibliographic record
Abstract
Abstract Social–emotional skills are vital for individual development, yet research on which skills most effectively promote students' mental and physical health, particularly from a global perspective, remains limited. This study aims to address this gap by identifying the most important social–emotional skills using global data and machine learning approaches. Data from 61,585 students across nine countries, drawn from the OECD Social–Emotional Skills Survey, were analyzed (N China = 7246, N Finland = 5482, N Colombia = 13,528, N Canada = 7246, N Russia = 6434, N Turkey = 5482, N South Korea = 7246, N Portugal= 6434, and N USA= 6434). Six machine learning techniques—including Random Forest, Logistic Regression, AdaBoost, LightGBM, Artificial Neural Networks, and Support Vector Machines—were employed to identify critical social–emotional skills. The results indicated that the Random Forest algorithm performed best in the prediction models. After controlling for demographic variables, optimism, energy, and stress resistance were identified as the top three social–emotional skills contributing to both subjective well‐being and physical health. Additionally, sociability and trust were found to be the fourth most important skills for well‐being and physical health, respectively. These findings have significant implications for designing tailored interventions and training programs that enhance students' social–emotional skills and overall health.
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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 | 0.003 | 0.007 |
| 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) | 0.001 | 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".