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Record W3133889777 · doi:10.1017/s1368980021001002

Food insecurity and housing affordability among low-income families: does housing assistance reduce food insecurity?

2021· article· en· W3133889777 on OpenAlexaff
Bo Kyong Seo, Gum‐Ryeong Park

Bibliographic record

VenuePublic Health Nutrition · 2021
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsMcMaster University
Fundersnot available
KeywordsFood insecurityFood securityCashPublic housingBusinessPovertySupplemental Nutrition Assistance ProgramLow incomeWelfareHousehold incomeEnvironmental healthEconomicsSocioeconomicsEconomic growthGeographyFinanceMedicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Given the competing needs for food and housing under the limited household income among poor families, there is lack of research on the associations between housing affordability and food insecurity. The current study examines how housing cost burden affects food insecurity of low-income families and whether decreased housing cost enhances food security. DESIGN: Longitudinal data from the Korean Welfare Panel Study, of which the final sample for the analysis consisted of 31 304 household-level observations from 5466 households based on twelve waves (2007-2018). SETTING: South Korea. PARTICIPANTS: Low-income households in the lowest 40 % of household income distribution. RESULTS: 19·3 % had food insecurity, and housing cost burden was associated with food insecurity. While in-kind housing assistance and in-cash assistance from all sources were likely to reduce food insecurity partially through influencing housing cost burden, in-cash housing assistance was associated with higher likelihood of food insecurity. CONCLUSIONS: Housing cost burden potentially limits food access among poor families, and housing assistance, particularly public housing and sufficient in-cash assistance, is conducive to alleviating food insecurity.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science 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 score0.087
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0050.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.002
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 head0.120
GPT teacher head0.393
Teacher spread0.273 · 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

Citations29
Published2021
Admission routes1
Has abstractyes

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