An exploration of the associations between immigration status, racialized status and depressive symptoms: An epidemiological study guided by Intersectionality Theory
Bibliographic record
Abstract
Background: Disparities in adolescents' mental health in Canada stem from many environmental, genetic and social factors. Intersectionality Theory explains how people exist at the intersection of a combination of social identities including a person’s racial group, immigrant status, socioeconomic status (SES), and gender. Depressive symptoms can vary across such social identity factors, prompting exploration of their synergistic effects. This study delves into how the intersection of immigrant identity and race influences depressive symptom risk among Canadian adolescents. Objectives: 1) Describe the prevalence and risk profiles of depressive symptoms across various social identity factors. 2) Explore if the joint effect of immigration and race on depressive symptoms exceeds the risk of the sum of the two main effects produced by these variables independently. Methods: Data from the 2017/2018 Health Behaviour in School-aged Children survey involving Canadian grades 6-10 were used (n=20,108). Objective 1 assessed depressive symptom patterns for various social identity factors using proportions and estimates of relative risk. Objective 2 employed adjusted log-binomial regression to measure the interaction between immigration and race; relative risk estimates, relative excess risk due to interaction, synergy index, and attributable proportion were also obtained. Results: The interaction of race and immigration yields a unique protection. Racialized immigrant youth exhibit significantly lower depressive symptom risk (RR=0.81, 95% CI 0.73, 0.98) than non-immigrant, non-racialized peers. Among the racialized subgroup, immigration associates with substantial risk reduction of depressive symptoms (RR=0.64, 95% CI 0.50, 0.77). Among immigrant youth, racialized individuals show a non-significant risk difference when compared to their non-racialized counterparts (RR=0.76, 95% CI 0.10, 1.42). The interaction between racialized and immigrant identity produced a significant risk reduction of depressive symptoms beyond additivity. The relative excess risk due to interaction (RERI) was -0.52 (95% CI -0.73, -0.30). Conclusions: The findings highlight a protective synergy of these two factors, indicating substantial risk reduction beyond the individual influences. These insights emphasize the need for further exploration of synergistic effects and the use of Intersectionality Theory when assessing health risks for youth. Tailored interventions and support strategies, which consider the intersectionality of multiple social identities to enhance adolescent mental well-being, are indicated.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".