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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".