Food Insecurity in Canadian Adults: Receiving Diabetes Care
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".