Factors Affecting the Mental Wellbeing of Immigrants/Racialized Communities in Canada: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Among the G7 nations, Canada has the highest proportion of foreign-born residents, with one in five Canadians being born abroad. Research indicates that immigrants/racialized communities often experience emotional and mental health challenges upon arrival in Canada. Although there are studies on the factors of common mental health issues, a systematic review with metaanalysis to identify the factors associated with anxiety and depression among immigrants/racialized communities specific to Canada is lacking. This study aims to systematically review and meta-analysis the factors associated with the prevalence of anxiety and depression among immigrants/racialized communities in Canada. Objective(s): We will conduct a systematic review of quantitative studies about anxiety and depression in immigrants/racialized communities. We will perform meta-analyses of each identified factor (beta coefficient) to obtain their aggregate estimates and explore sources of heterogeneity through a metaregression. Methods: Five electronic databases (MEDLINE, PsycINFO, EMBASE, CINAHL, ProQuest) will be searched using a set of keywords. Two independent reviewers will screen articles based on titles, abstracts, and full texts. Additional articles will be added through Grey literature search. We will assess the quality of the selected study using the Newcastle and Ottawa Scale (NOS) for studies reporting prevalence data. We will perform a meta-analysis and meta-regression of the variability of the factors associated with the prevalence of anxiety and depression. Also, a meta-regression of individual factors' beta coefficient variability will be performed. Results: This study will identify the important factors that are associated with anxiety and depression among immigrant/racialized communities. Data will be presented in tabular form and graphically using a forest plot. Also, publication bias will be assessed using a funnel plot. The identified study’s quality will be assessed and classified based on quality indicators of the NOS. Conclusion: This study will help us to understand the important factors associated with the prevalence of anxiety and depression among immigrant/racialized communities in Canada. The results of this study will also be important to highlight gaps in the current evidence base and priorities for future research directions
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.028 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".