What Really Matters for Social Adaptation Among Left-Behind Children in China? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Compared with non-left-behind children, left-behind children in China have lower social adaptation and theunderlying reasons deserve further study. This systematic review and meta-analysis included 29 studies publishedbetween 2006 and 2019. Protective factors of Left-behind children's social adaptation were resilience (r=0.574),self-efficacy (r=0.538), emotional intelligence (r=0.421), self-esteem (r=0.404), positive coping (r=0.471),attachment (r=0.354) and social support (r=0.338) while risky factors were loneliness (r=- 0.453) and socialanxiety(r=-0.360). Age, birthplace, father/mother migration and the frequency of parent-child communication alsohave a certain impact on their social adaptation, but the effect size is relatively small. This study can provide someenlightenment for intervention programs and policy adjustment. More empirical studies focusing on influencingfactors for social adaptation of left-behind children will be needed in the future.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".