Disparities in Insulin Pump Use Among Spanish-Speaking Children With Type 1 Diabetes Compared to Their Non-Hispanic White Peers: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Disparities in Insulin Pump Use Among Spanish-Speaking Children With Type 1 Diabetes Compared to Their Non-Hispanic White Peers: Mixed Methods Study. OBJECTIVE: We aimed to investigate the use of insulin pumps and continuous glucose monitoring (CGM) devices among Spanish-language-preferring children in our clinic population and to identify specific barriers to technology use. METHODS: First, we assessed rates and patterns of diabetes technology use (eg, insulin pumps and CGM devices) in a sample of 76 children (38 Spanish-language preferring and 38 non-Hispanic White). We compared rates of technology use, average length of time between diabetes diagnosis and initiation of insulin pump or CGM device, and rates of discontinuation of these devices between the Spanish-language-preferring and non-Hispanic White children. Second, to understand specific barriers to technology use, we compared responses to a questionnaire assessing decision-making about insulin pumps. RESULTS: Spanish-language-preferring patients had lower rates of insulin pump use, even after controlling for age, gender, age at diagnosis, and type of health insurance. Spanish-language-preferring participants were more likely to report concerns over learning to use an insulin pump and were more likely to discontinue using an insulin pump after starting one. CONCLUSIONS: These data confirm demographic disparities in insulin pump use among children with T1D and provide new insights about insulin pump discontinuation among Spanish-language-preferring children. Our findings suggest a need for improved patient education about insulin pump technology in general and improved support for Spanish-language-preferring families with T1D after initiation of pump therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".