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Record W2022141305 · doi:10.3148/73.3.2012.e261

Food Insecurity in Canadian Adults: Receiving Diabetes Care

2012· article· en· W2022141305 on OpenAlexafffundvenueabout
Suzanne Galesloot, Lynn McIntyre, Tanis R. Fenton, Sheila Tyminski

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2012
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of CalgaryAlberta Health Services
FundersUniversité de Montréal
KeywordsFood insecurityMedicineEnvironmental healthDiabetes mellitusFood securityPopulationConfidence intervalYoung adultHealth careGerontology

Abstract

fetched live from OpenAlex

PURPOSE: The prevalence of adult-level household food insecurity was examined among clients receiving outpatient diabetes health care services. METHODS: Participants were adults diagnosed with diabetes mellitus, who attended individual counselling sessions at Calgary's main clinic from January to April 2010. Clinicians were trained to administer the Household Food Security Survey Module (HFSSM), and did so with clients' assent during their scheduled sessions. RESULTS: The prevalence of adult-level household food insecurity among 314 respondents was 15.0% (95% confidence interval [CI], 11.2 to 19.4); 6.7% (95% CI, 4.2 to 10.0) of clinic attendees were categorized as severely food insecure. The comparable rates obtained in Alberta in 2007 using the same instrument (HFSSM) were 5.6% and 1.2%, respectively. CONCLUSIONS: Household food insecurity rates among individuals with diabetes in active care are higher than rates reported in Canadian population surveys. Severe food insecurity, indicating reduced food intake and disrupted eating patterns, may affect this population's ability to follow a pattern of healthy eating necessary for effective diabetes management. This study reinforces the importance of assessing clients' inability to access food because of financial constraints, and indicates that screening with a validated measure may facilitate identification of clients at risk.

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)0.006
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.003
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 head0.220
GPT teacher head0.500
Teacher spread0.280 · 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

Citations46
Published2012
Admission routes4
Has abstractyes

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