The Relationship of Acculturation Strategies to Resilience: The Moderating Impact of Social Support among Qiang Ethnicity following the 2008 Chinese Earthquake
Bibliographic record
Abstract
International research has mostly confirmed the positive association between acculturation strategies and resilience in ethnic groups, but the mediating and moderating mechanisms underlying the relationships are still under-investigated. The present study aimed to investigate the associations between acculturation strategies (based on two cultural identities) and resilience of 898 Qiang ethnicity volunteers (mean age = 29.5), especially exploring the mediating and moderating effects of personality, spiritual belief and social support on the relationship between acculturation strategy (using two cultural identities as latent variables in model analysis) and resilience following the occurrence of 2008 Wenchuan earthquake in Sichuan, taking such mechanisms into account. Results were as follows: (1) All variable presented significant positive correlations; (2) Consistent with the mediating hypotheses, personality and spiritual beliefs played a partial mediating role in the relationship between two cultural identities and resilience; (3) High or low level of perceived social support had a moderating effect on cultural identities and resilience; (4) The integration strategy was the most optimal style to promote the development of resilience, but marginalization was the least effective style.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".