Chinese immigrant children : predictors of emotional and behavioural problems
Bibliographic record
Abstract
Most recent Canadian studies on the mental health and behavioural problems of Canadian immigrant children have focused on the impact of various socioeconomic and demographic factors. To better understand the emotional and behavioural problems of immigrant children, it is important to look beyond the children's family demographics and to assess the broader social context. The current study explored the effects of immigrant children's social relationships within families and peer groups, as well as the effects of their demographic backgrounds, on the children's behavioural problems. This paper is based on the data for 182 Chinese immigrant children aged 11 to 13 years old collected from the New Canadian Children and Youth Study (NCCYS) 1st Wave in Montreal. Measures of the social relationships and behavioural problems include the following three tools: children's perceptions of their emotional and behavioural problems scales (five subscales); children's perception of parental relationships (parental nurturance, parental rejection, and relationships with parents); peer relationships (social competence, involvement with peers in trouble, and participating in bullying). The regression results indicated that immigrant children's relationships with both parents and peers were the most significant predictor of specific behaviour problems. Demographic factors, especially family structure, gender, and ethnicity, were also found to influence behavioural problems of Chinese immigrant children. In order to improve the integration and adaptation process for immigrant children and their families with adjustment difficulties in their social relationships and behavioural problems, relevant intervention and prevention programs (including early identification of children at risk, developing pro-social skills, improving parent-child interaction skills) need to be developed in school settings in collaboration with the community, by government, and by ethno-specific community groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".