MétaCan
Menu
Back to cohort
Record W4394852325 · doi:10.1177/08901171241246842

Exploring the Impact of Length of Residence and Food Insecurity on Weight Status Among Canadian Immigrants

2024· article· en· W4394852325 on OpenAlexaboutno aff
Lei Chai

Bibliographic record

VenueAmerican Journal of Health Promotion · 2024
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsFood insecurityImmigrationResidenceEnvironmental healthGerontologyDemographyMedicineGeographyPsychologyFood securitySociology

Abstract

fetched live from OpenAlex

Purpose While the individual impacts of long-term residence and food insecurity on overweight/obesity are well-documented, their combined effect on immigrants’ weight status is less understood. This study examines the interaction between length of residence and food insecurity in predicting overweight/obesity among immigrants and investigates whether this relationship is gender-specific. Design A national cross-sectional survey. Setting The 2017-2018 Canadian Community Health Survey. Subjects Immigrants aged 18 and older (N = 13 680). Measures All focal variables were self-reported. Analysis Logistic regression models were employed. Results Long-term immigrants were more likely to report overweight/obesity than their short-term counterparts (OR = 1.39; P < .001). Moreover, immigrants from food-insecure households were at a higher risk of reporting overweight/obesity (OR = 1.27; P < .05) compared to those from food-secure households. The analysis further revealed that food insecurity exacerbated the detrimental association between length of residence and overweight/obesity in men (OR = 2.63; P < .01) but not in women (OR = .66; P > .05). Conclusion The findings suggest that long-term immigrant men may be especially susceptible to the compounded chronic stressors of extended residence and food insecurity. Health professionals and policymakers should advocate for psychosocial resources to help mitigate these adverse effects and support the well-being of immigrant populations.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.078
GPT teacher head0.369
Teacher spread0.291 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Health PromotionSame topicMigration, Health and TraumaFrench-language works237,207