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Record W4386595690 · doi:10.1097/fch.0000000000000377

Food Insecurity and Health

2023· article· en· W4386595690 on OpenAlexaffabout
Lei Chai

Bibliographic record

VenueFamily & Community Health · 2023
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of TorontoStatistics Canada
Fundersnot available
KeywordsFood insecurityModerationMental healthPsychologyEnvironmental healthCoping (psychology)Marital statusFood securitySurvey data collectionSocial psychologyMedicineGeographyPopulationClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Existing research has established the detrimental effects of food insecurity on health. However, understanding of the social conditions that may moderate this relationship remains limited. To address this gap, the study investigates two questions: First, does marital status moderate the association between food insecurity and self-rated health? Second, if such moderation exists, does its impact vary based on gender? Data from the 2017-2018 Canadian Community Health Survey, a nationally representative survey conducted by Statistics Canada (n =101 647), were utilized for this investigation. The findings demonstrated that individuals living in food-insecure households reported poorer self-rated mental and general health. However, the negative impact of food insecurity on both health outcomes was less pronounced among married individuals than among their unmarried counterparts. Furthermore, the stress-buffering role of marriage was found to be more substantial among men than among women. In light of the significant stress-buffering role of marriage revealed in this study, it is crucial for policies to aim at providing comparable coping resources to unmarried individuals, particularly women.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.510
GPT teacher head0.531
Teacher spread0.021 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2023
Admission routes2
Has abstractyes

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