6.10-P13Acculturation, ethnic identity, and psychological well-being of Albanian-American immigrants in the United States
Bibliographic record
Abstract
This study examined the relationship between acculturation, ethnic identity, and psychological well-being of the Albanian-American immigrant community in United States. A total of 139 Albanian-American immigrants aged 21-35 years old participated in the study. In order to utilize the data, participants filled out four different surveys, including a demographic questionnaire, Multigroup Ethnic Identity Measure (MEIM), Vancouver Index of Acculturation (VIA), and Ryff’s Psychological Well-Being scale. A correlational design relying on cross-sectional survey data and multiple regression analyses were used to study the correlations between acculturation, ethnic identity, and psychological well-being. The results showed that ethnic identity, acculturation, and psychological well-being were positively correlated to each other. In addition, the results showed that both ethnic identity and acculturation affected the psychological well-being of Albanian-American immigrants in the United States. This relationship was further moderated by gender and length of residency in the United States and mediated through graduate school education. The results of this study will help clinicians, social workers, and policy makers that work with immigrants to better understand the psychological consequences of immigration due to acculturation and ethnic identity factors. This study provides references about the process of acculturation, specifically how immigrants understand and navigate their new environments.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".