Life Satisfaction in Canada and the Immigrant Experience
Bibliographic record
Abstract
Abstract High-income countries like Canada have the highest self-reported levels of life satisfaction in the world, although little is known regarding the experiences of racialized people and immigrants to these countries. This study investigates the factors that influence subjective well-being among a large national cohort study of 8,063 adults from the Canadian Alliance of Healthy Hearts and Minds cohort study recruited between 2014 and 2018, including a subset of 2,142 immigrants. Measures of demographic, socioeconomic, health, healthcare access, and self-reported ethnicity, from which racialized status was derived, were investigated in relation to self-reported life satisfaction as measured by the validated Cantril ladder score. Among 8,063 adults average age of 58 years, approximately half were women, 18.6% were racialized, and 26.6% were immigrants. Racialized immigrants had significantly lower life satisfaction compared to non-racialized immigrants and Canadian-born persons, whether racialized or not. Multivariable analysis showed that factors associated with higher life satisfaction included older age, male sex, having trusted neighbours, and having a language-concordant family doctor. Factors associated with lower life satisfaction included being racialized, having a higher social disadvantage, poorer cardiovascular health, and being unable to afford prescription medications. Amongst immigrants, those racialized were more likely to report experiencing discrimination based on skin colour and reported lower life satisfaction. Although high income countries like Canada have amongst the highest life satisfaction scores in the world, racialized people, especially immigrants, have lower life satisfaction compared to non-racialized people. Key Messages This study examines associations of demographic, socioeconomic, health, healthcare access that influence self-reported life satisfaction among adults in Canada, particularly among racialized individuals and immigrants. Using data from the Canadian Alliance of Healthy Hearts and Minds cohort study, we found racialized immigrants report significantly lower life satisfaction compared to non-racialized immigrants and Canadian-born individuals. Despite Canada’s high average life satisfaction, the data suggests there is still a need for equity-focused and culturally tailored healthcare to improve well-being among structurally marginalized groups.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".