Family Matters: How Family Structure and Cohesion Shape Immigrant Adolescents’ Well-Being
Bibliographic record
Abstract
This study examines how family structure and cohesion affect immigrant adolescents’ well-being (depression, self-esteem, and academic performance), using data from the Children of Immigrants Longitudinal Study (CILS), ( N = 3,413, Mage = 14.15, SD = 0.83, male 47%, and female 53%). Guided by Family Strength Theory, linear multiple regression analyses revealed that family cohesion served as a key mediator between family structure and well-being, while its moderating effect was limited. Adolescents from intact families reported higher self-esteem and academic performance but also slightly higher depression scores compared to peers from single-parent or blended families. Single-parent and blended family structures were positively associated with depression and negatively associated with academic performance, though associations with self-esteem were weak or nonsignificant. Gender differences were evident that females reported higher depression and slightly lower self-esteem but outperformed males academically. Ethnicity showed small but significant effects, with certain backgrounds linked to slightly higher depression scores, while ethnicity was positively related to self-esteem. Higher parental acculturation was associated with slightly more depression. Mother’s education level was negatively related to self-esteem, whereas father’s education and parental acculturation were nonsignificant predictors. Both parents’ education levels were positively associated with academic achievement, while parental acculturation demonstrated a small but significant negative association.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".