Food Insecurity Screening Among Families of Children With Diabetes
Bibliographic record
Abstract
Little is known about screening in clinical settings for food insecurity (FI) among households of children with diabetes. This study evaluated the acceptability and feasibility of an FI screening initiative in a pediatric diabetes clinic that was implemented to help diabetes dietitian educators tailor management plans for families of children with type 1 or type 2 diabetes facing FI. The initiative comprised three validated screening questions, a care algorithm, a community resource handout, and a poster. In total, 50 families of children and adolescents aged 0-18 years with type 1 or type 2 diabetes were screened for FI. In-person semi-structured interviews combining open-ended and Likert-scale questions were conducted with 37 of the screened families and the three diabetes dietitian educators who conducted the screening. Perceived barriers and facilitators of the screening initiative were identified using content analysis, and Likert-scale questionnaires rated interviewees' comfort level with the screening questions. A reflective journal kept by an onsite research interviewer also facilitated the data interpretation process. Most families felt comfortable answering the screening questions. Families with FI appreciated the opportunity to express their concerns and learn about affordable food resources. However, ∼20% of these families described stigma and fear of judgment by clinicians if they screened positive for FI. Diabetes educators also felt comfortable with the screening questions but reported lack of time to screen all families and to follow-up with resources after a positive screen. A self-reported intake form was recommended to ensure that everyone is systematically screened. A standardized and respectful method of assessing FI could help clinicians better tailor treatment plans and support for families of children with diabetes who face FI. Based on these findings, similar FI screening initiatives should be implemented in other clinical settings as part of routine clinical practice.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".