Self‐concept of left‐behind children in <scp>C</scp>hina: a systematic review of the literature
Bibliographic record
Abstract
The aim of our study was to systematically review studies which had compared self-concept in left-behind children with the general population of children in China. Relevant studies about self-concept of left-behind children in China published from 2004 to 2014 were sought by searching online databases including Chinese Biological Medicine Database (CBM), Chinese National Knowledge Infrastructure (CNKI), Wanfang Database, Vip Database, PubMed Database, Google Scholar and Web of Science. The methodological quality of the articles was assessed by using Newcastle-Ottawa Scale (NOS). Poled effect size and associated 95% confidence interval (CI) were calculated using the random effects model. Cochrane's Q was used to test for heterogeneity and I(2) index was used to determine the degree of heterogeneity. Nineteen studies involving 7758 left-behind children met the inclusion criteria and 15 studies were included in a meta-analysis. The results indicated that left-behind group had a lower score of self-concept and more psychological problems than the control group. The factors associated with self-concept in left-behind children were gender, age, grade and the relationships with parents, guardians and teachers. Left-behind children had lower self-concept and more mental health problems compared with the general population of children. The development of self-concept may be an important channel for promoting mental health of left-behind children.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".