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Record W2922286455 · doi:10.3390/su11061571

“Everybody I Know Is Always Hungry…But Nobody Asks Why”: University Students, Food Insecurity and Mental Health

2019· article· en· W2922286455 on OpenAlexafffundabout
Nayantara Hattangadi, Ellen Vogel, Linda Carroll, Pierre Côté

Bibliographic record

VenueSustainability · 2019
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of AlbertaOntario Tech University
FundersCanadian Institutes of Health ResearchOntario Trillium Foundation
KeywordsFood insecurityMental healthFocus groupnobodyPsychologyQualitative researchFood securityFace (sociological concept)Environmental healthSociologyMedicineGeographyPsychiatryAgricultureSocial science

Abstract

fetched live from OpenAlex

Food insecurity is a substantial problem in Canadian university students. Multiple cross-sectional studies suggest that nearly a third of university students across Canada report food insecurity. Yet, little is understood about the experiences of food-insecure students and the impact of their experiences on their mental health. To address this, a multi-method study was conducted using quantitative and qualitative approaches to describe the prevalence, association and experience of food insecurity and mental health in undergraduate students. The current paper reports on the qualitative component, which described the lived experiences of food-insecure students, captured through face-to-face focus group interviews with participants (n = 6). The themes included (1) contributing factors to food insecurity; (2) consequences of food insecurity; and (3) students’ responses/attempts to cope with food insecurity. The findings illuminated student voices, added depth to quantitative results, and made the experience of food insecurity more visible at the undergraduate level. Additional research is needed to understand students’ diverse experiences across the university community and to inform programs to support students.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.406
Teacher spread0.364 · 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

Citations36
Published2019
Admission routes3
Has abstractyes

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