Self-Esteem, Resilience, Social Support, and Acculturative Stress as Predictors of Loneliness in Chinese Internal Migrant Children: A Model-Testing Longitudinal Study
Bibliographic record
Abstract
The present study examined the risk and protective factors of loneliness among Chinese internal migrant children (CIMC) in Beijing, China, including self-esteem, resilience, social support, and acculturative stress. Longitudinal survey data were collected from a large sample of 4th, 5th, and 6th grade CIMC from three schools in Beijing, at four time points (N = 862 at T1 to N = 837 at T4) over a 20-month period. Grounded in the Cultural and Contextual Model of Coping and the Acculturation Theory, two predictor models of loneliness were tested with path analysis. The results yielded the following: a) the two predictor models fit the data well; b) CIMC’s T1 self-esteem and T1 resilience protected them against loneliness at T4; and c) CIMC’s T2 social support seeking was a significant mediator between self-esteem and loneliness, and between resilience and loneliness; and d) similarly, CIMC’s T3 acculturative stress was a significant mediator between self-esteem and loneliness, and between resilience and loneliness. The study’s results highlight the merit and importance of implementing theoretically-guided, model-testing research grounded in a prospective research design, to help advance CIMC research. Implications for future research on and practical support for CIMC are discussed.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".