General and acculturation‐related daily hassles and psychological adjustment in first‐ and second‐generation South Asian immigrants to Canada
Bibliographic record
Abstract
According to Lay and Nguyen (1998), in addition to the general daily hassles encountered by most people, immigrants often face chronic difficulties specific to the acculturation experience, including conflicts with family members, members of the ethnic ingroup, and members of ethnic outgroups. Moreover, it has been suggested that the children of immigrants born in Canada (i.e., second‐generation immigrants) may experience different acculturative stressors from their parents (i.e., first‐generation immigrants). This study examined general and acculturation‐related daily hassles in 74 first‐ and second‐generation South Asians in Canada. Participants completed a questionnaire that assessed their experience of different types of daily hassles (general, family, ingroup, and outgroup), acculturation attitudes, and level of psychological adjustment. Second‐generation individuals reported significantly more ingroup hassles and marginally lower self‐esteem than first‐generation immigrants. For first‐generation immigrants, more ingroup hassles predicted greater depression, and for second‐generation individuals, increased ingroup hassles predicted lower self‐esteem and more outgroup hassles predicted greater depression. The results emphasize the importance of considering the acculturation experience of second‐generation individuals as being unique to that of first‐generation immigrants.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".