Being Brazilian, Becoming Canadian: Acculturation Strategies, Quality of Life, Negative Affect, and Well-being in a Sample of Brazilian Immigrants Living in the Greater Toronto Area
Bibliographic record
Abstract
Acculturation is a predominant feature of today’s society and one that has unique implications for immigrants’ mental health. Given that two thirds of Canada’s population growth is due to immigration, understanding the effects of acculturation on newcomers should be a central focus of academic research. The present study utilized an exploratory quantitative method to investigate the associations between acculturation strategies, quality of life, and negative affect in a sample of 180 Brazilian immigrants living in the Greater Toronto Area. The mediating and moderating roles of quality of life (QOL) were explored, as well as which patterns of acculturation strategies were associated with enhanced well-being, represented by low negative affect (NA), high QOL, and high Satisfaction with Life in Canada (SLCI). Clusters analysis identified patterns of acculturation strategy use, resulting in four acculturation profiles: integrated, assimilated, separated, and marginalized. Results indicated that QOL did not act as either a mediator or moderator of the relationship between acculturation profiles and NA. With regard to well-being indicators, acculturation profiles successfully predicted NA and SLCI, with the Assimilated being the most favourable profile evidenced by its lowest NA and highest SLCI levels. While acculturation profiles did not predict QOL, the trend of the Assimilated profile being predictive of favourable well-being was also present as its members reported slightly higher QOL than their counterparts from other profiles. Well-being risk and protective factors are presented. The results highlight the importance of including control variables in future research in order to uncover the unique impact of acculturation on the mental health of immigrants. Implications for practice and future research are also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| 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".