Identity Profiles and Well-Being of Multicultural Immigrants: The Case of Canadian Immigrants Living in Quebec
Bibliographic record
Abstract
Studies worldwide point toward increased risk of mental health issues among immigrants. Immigrants' ability to integrate the cultural identity of their new country has been found to be a key factor in their psychological well-being. Even though researchers agree on the crucial role of identity integration in immigrants' well-being, the current literature has two main limitations: (1) researchers do not agree on the importance that should be allocated to each of the cultural identities, and (2) research has focused on bicultural individuals as opposed to multicultural individuals. The present paper proposes to study Canadians immigrants living in the province of Quebec who, because of the political and linguistic situation of the province, face the challenge of integrating two new cultural identities (Quebecer and Canadian) to their original one. Specifically, cluster analysis was used to observe identity profiles that naturally emerge among 120 Canadian immigrants from the province of Quebec. Identity profiles were then compared on various indices of well-being to identify the optimal identity structure. In total, four identity profiles emerged, differing in their levels of identity coherence (i.e., similar levels of identification with each group) and identification to either the original group or the Quebecers. ANOVA results confirmed that identity profiles differed in their average level of well-being. First, immigrants with coherent profiles displayed higher levels of well-being. Second, among incoherent profiles, the profile where identification to the original group is the highest showed the greatest well-being. Implications suggest that in order to maximize immigrants' well-being, psychologists should focus on the coherence between cultural groups as well as identification to the original group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".