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Enregistrement W6959147348 · doi:10.7939/r3-c5c4-8z92

We are not all the same: Differentials in Diet Quality and Food Consumption across Canadian Immigrant and Domestic Resident Population

2023· dissertation· en· W6959147348 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity of Alberta Library · 2023
Typedissertation
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueGenetic Associations and Epidemiology
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésImmigrationResidenceOddsEthnic groupLogistic regressionPopulationModerationOdds ratio

Résumé

récupéré en direct d'OpenAlex

Background: The association between diet and immigration is multidimensional; in addition, it varies considerably, depending on the nutrient or food component in question and more importantly, the ethnicities of immigrants to Canada. Current literature identifies dietary patterns in the general Canadian population, yet little is known about dietary heterogeneity among the adult immigrant population. Since the burden of chronic, non-communicable diseases (NCDs) falls disproportionately on immigrants and ethnic minorities, examining the dietary patterns of these populations may offer a framework for health promotion among Canada’s most vulnerable groups. Purpose: This research investigated the association of immigration status and length of residence in Canada with dietary patterns among Canadian adults. Methods: Data from the 2015 Canadian Community Health Survey (CCHS) Nutrition was used. A Canadian adaptation of Healthy Eating Index (C-HEI) 2015 based on the 2007 Canada’s Food Guide (CFG) was used as an indicator of diet quality and adherence to dietary recommendations. Descriptive analyses examined C-HEI mean scores for demographic characteristics by immigration status and length of residence. The association of immigration status and length of residence in Canada with dietary patterns were examined using logistic regression models. The odds of good diet quality and odds of adherence to recommended guidelines for adequacy components (vegetables and fruit, whole fruit, greens and beans, whole grains, dairy, total protein foods, seafood and plant proteins, and fatty acids) and moderation components (refined grains, sodium, added sugars, and saturated fats) were generated from the models, adjusted for covariates of interest in the study. Results: For the population aged 20 to 79, the average C-HEI 2015 score was 62.99 out of a possible 100 points. We observed heterogeneity in diet quality and food consumption across immigrants and domestic residents of Canada. Immigrants had significantly higher C-HEI scores compared with Canadian-born (65.06[0.25], 62.19[0.12]; p<0.001) and more favourable intakes for many of the score components. Immigrants showed a greater likelihood of achieving a good diet and adherence to recommendations for vegetables and fruit, whole fruit, whole grains, seafood and plant proteins, dairy products, refined grains, sodium, added sugars, and saturated fats. Even so, consumption of greens and beans was low in the immigrant diet. Among immigrant groups, our results suggest that ethnic visible minorities, namely Black, East/Southeast Asian, West Asian/Arab, South Asian, Latin American, and Other have a nutritional health advantage over not only White but their Canadian-born counterparts. Among domestic residents, however, White had a health advantage over most Canadian-born visible minorities. Length of residence strongly affected dietary habits, with both negative and positive effects observed. The main trend after a longer stay in Canada was a substantial increase in the likelihood of fulfilling recommendations for greens and beans, seafood and plant proteins, refined grains and added sugars. On the other hand, we observed a decrease in consumption of dairy, total protein foods, and fatty acids as well as an increasing trend in consumption of saturated fats after a longer stay in Canada. For immigrant men, we observed an acculturation-driven trend in their consumption of vegetables and fruit and refined grains. For immigrant women, our findings suggested an acculturation-driven trend in their consumption of seafood and plant protein, refined grains, and added sugars. Our results also showed that immigrant women are more likely to be rewarded with a good overall diet quality as the length of residence increases. Irrespective of the length of residence, Black, East/Southeast Asian, West Asian/Arab, South Asian, and those identified as Other were more likely to have a good diet quality compared with White immigrant. Conclusions: The findings of this study postulate heterogeneous nutritional health advantages among Canada's population, as well as an overall “healthy immigrant effect” that is maintained through dietary habits with length of residence in Canada. The results challenge research that portrays immigrants as one broad category when investigating the “healthy immigrant effect” in relation to dietary acculturation. This approach not only undermines the heterogeneity in dietary patterns but also underscores immigrants’ abilities to maintain healthy eating patterns, especially among adult immigrants, with a longer residence in Canada. Results further justify a need for tailoring educational interventions to specific ethnic and racial groups, and adaptation to CFG that is more inclusive.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,019
Score d'incertitude au seuil0,086

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,003
Études des sciences et des technologies0,0040,002
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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.

Tête enseignante Opus0,023
Tête enseignante GPT0,271
Écart entre enseignants0,248 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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