Who thrives in Canada? An Examination of social factors, healthcare access, and immigration status
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».