Household Food Insecurity Among Older People in Canada: the Exploration of a Public Health Issue Rendered Invisible
Notice bibliographique
Résumé
Food insecurity, an issue of inadequate or insecure access to food due to financial constraints, is under-researched among older people. Of this limited literature, the relevance of aging to food insecurity remains unclear. My goals with this thesis were to: i) synthesize and critically examine the literature on food insecurity and aging; ii) contribute a profile of food insecurity among older people to the population estimates of food insecurity in Canada; and iii) examine more closely key predictors of food insecurity among older people in Canada. I undertook a scoping study, and characterized this collection of literature by methodological, empirical, and conceptual contributions. Population estimates varied greatly between studies, with an overall emphasis on individual risk and experience, alongside attempts to explicate a perceived complexity of this issue with respect to health. Of the very limited inclusion or application of theory overall, most emphasis was placed on frameworks of disease and disability, with many assumptions made about how aging relates to food insecurity. Taken together, these findings suggest a strong tendency towards biomedicalization. The two subsequent empirical studies involved the use of data from the Canadian Community Health Survey (CCHS). In the second manuscript I found the overall prevalence of food insecurity among older Canadians to be 2.4%, and using a generalized linear model in the second manuscript, I found sex, age, racial background, self-perceived health, marital status, living arrangement, and low household income to be predictive of food insecurity. In the third manuscript, logistic regression revealed that women are at greater risk of food insecurity and this inequity largely operates through household income. Younger old people were most vulnerable to food insecurity, and age modified the relationship between household income and food insecurity. In the discussion chapter I offer a brief political-historical context of hunger and aging in Canada, explore the relevance and implications of the major findings from each study, and demonstrate the connection between consistent monitoring of food insecurity in exploring the geographic heterogeneity of this issue. Overall, I argue that, like younger populations, food insecurity among older people is an income-based issue.
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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 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».