Developing and Implementing a Food Insecurity Screening Initiative for Adult Patients Living With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVES: Routine food insecurity screening is recommended in diabetes care to inform more tailored interventions that better support diabetes self-management among food-insecure patients. This pilot study explored the acceptability and feasibility of a food insecurity screening initiative within a diabetes care setting in Toronto. METHODS: A systematic literature review informed the development of a food insecurity screening initiative to help health-care providers tailor diabetes management plans and better support food-insecure patients with type 2 diabetes. Interviews with 10 patients and a focus group with 15 care providers elicited feedback on the relevance and acceptance of the food insecurity screening questions and a care algorithm. Subsequently, 5 care providers at 4 sites implemented the screening initiative over 2 weeks, screening 33 patients. After implementation, 7 patients and 5 care providers were interviewed to assess the acceptability and feasibility of the screening initiative. RESULTS: Our findings demonstrate that patients are willing to share their experiences of food insecurity, despite the sensitivity of this topic. Screening elicited information about how patients cope with food insecurity and how this affects their ability to self-manage diabetes. Care providers found this information helpful in directing their care and support for patients. CONCLUSIONS: Using a standardized, respectful method of assessing food insecurity can better equip health-care providers to support food-insecure patients with diabetes self-management. Further evaluation of this initiative is needed to determine how food insecurity screening can affect patients' self-management and related health outcomes.
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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 | 0.032 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".