The Effect of Left-Behind Experience and Self-Esteem on Aggressive Behavior in Young Adults in China
Bibliographic record
Abstract
There is little investigation on the independent effects of left-behind experience (LBE) on self-esteem and aggressive behavior in Chinese young adult populations, or the interaction effects of LBE and self-esteem on aggressive behavior. Thus, a school-based health survey was conducted in Anhui province in China in 2017. A total of 4,154 college students completed standard questionnaires which contain details of left-behind-related characters, self-esteem, aggressive behavior, and sociodemographic profile. Of included students, 55.3% were those with LBE (LBEs). Compared to students without left-behind experiences (NLBEs), LBEs had significantly increased scores of aggressive behavior and decreased score of self-esteem. The increased aggression in LBEs was highly related to longer left-behind duration, younger age of left-behind for the first time, and decreased self-esteem. On the other side, the aggressive behavior was demonstrated negatively correlated with self-esteem in both LBEs and NLBEs. There was an interaction effect of left-behind duration and self-esteem on physical aggression and of frequency of contacting with parents and self-esteem on verbal aggression. Besides, the interaction of primary caregiver and self-esteem on hostility and aggression toward self were also observed, respectively. Our results indicated LBEs and low self-esteem are associated with increased risk of aggressive behavior in Chinese young adults. The increased aggressive behavior in LBEs were highly related to longer left-behind duration, younger age of left-behind for the first time and decreased self-esteem. In those LBEs with some certain left-behind-related characters, aggressive behavior decreased more prominently with the increase of self-esteem. Strategies to improve self-esteem, particularly among young adults who have certain characters of LBE, should be a significant component of prevention and interventions of aggressive behavior.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".