Who thrives in Canada? An Examination of social factors, healthcare access, and immigration status
Bibliographic record
Abstract
High-income countries like Canada report some of the worlds' highest life-satisfaction levels, yet less is known about how life satisfaction varies by race and immigration status. This study investigates the factors that influence subjective well-being among 8,063 adults from the Canadian Alliance of Healthy Hearts and Minds study recruited between 2014 and 2018, including a subset of 2,142 immigrants. Measures of demographic, socioeconomic, health, healthcare access, and self-reported ethnicity were investigated in relation to self-reported life satisfaction as measured by the validated Cantril ladder score in which people were classified as suffering [1-4], struggling [5-6], or thriving [7-10]. Among 8,063 adults, approximately half were women, 18.6% were racialized, and 26.6% were immigrants. The mean life satisfaction score was 7.2 (1.4), with 71% classified as thriving. However racialized immigrants reported significantly lower life satisfaction than Canadian born non-racialized participants [6.6 (1.6) vs 7.2 (1.4); P < 0.001, and a lower proportion were classified as thriving [57% vs 73%]. In the overall sample, multivariable linear regression showed higher life satisfaction was associated with older age, male sex, having trusted neighbours, and having a language-concordant family doctor. Lower life satisfaction was associated with social disadvantage, being female, having poorer cardiovascular health, being unable to afford prescription medications, seeking care in an emergency department, and being racialized. Amongst the subset of immigrants, the life satisfaction associated factors were directionally consistent and racialized immigrants reported lower life satisfaction due to discrimination based on skin colour. Although Canada has amongst the highest life-satisfaction scores globally, the average masks persistent inequities as racialized people (especially racialized immigrants) have lower life satisfaction than non-racialized people. The findings highlight actionable levers-language-concordant primary care attachment, affordable medications, neighbourhood trust, and improved cardiometabolic health-that can be targeted to close the observed well-being gap.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".