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Record W4407144702 · doi:10.1177/08982643251314066

Unpacking the Association Between Food Insecurity and Mental Health Disorders Among Older Adults

2025· article· en· W4407144702 on OpenAlexaboutno aff
Lei Chai, Xiangnan Chai

Bibliographic record

VenueJournal of Aging and Health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
FundersNational Social Science Fund of China
KeywordsMental healthFood insecurityPsychological interventionAnxietyMoodPsychologyGerontologyAssociation (psychology)Logistic regressionClinical psychologyEnvironmental healthMedicinePsychiatryFood securityGeography

Abstract

fetched live from OpenAlex

Objectives Previous research shows a negative correlation between food insecurity and mental health, but limited exploration exists among older adults. This study examines this association in Canadian adults aged 65 and older, focusing on the mediating roles of perceived life stress and community belonging, and the moderating role of gender. Methods Cross-sectional data from the 2017–2018 Canadian Community Health Survey ( n = 28,044) were analyzed using logistic regression. Results The associations between food insecurity and both anxiety and mood disorders were partially mediated by high life stress and low community belonging. The adverse associations of food insecurity, high life stress, and low community belonging with an anxiety disorder were more pronounced in women than in men. Similar patterns were observed for a mood disorder. Discussion Interventions should address food insecurity, life stress, and community belonging, with particular attention to the unique challenges faced by older women to improve mental health.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.062
GPT teacher head0.427
Teacher spread0.366 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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