Social disadvantage and technology use among adults with type 1 diabetes in Quebec: A cross‐sectional study using data from the Canadian <scp>T1D</scp> ( <scp>BETTER</scp> ) Registry
Bibliographic record
Abstract
AIMS: We evaluated associations between social disadvantage and insulin pump and continuous glucose monitor (CGM) use among adults with type 1 diabetes (T1D) in Quebec, Canada, where public funding is available for CGM but not for insulin pumps. MATERIALS AND METHODS: We conducted a cross-sectional analysis using self-reported survey data collected from April 2019 to October 2023. Primary exposures were social disadvantage indicators (Race, income, education, employment, insurance, immigration, rural/urban location). Primary outcomes were insulin pump and CGM use. Logistic regression was used to assess associations between social disadvantage indicators and the odds of insulin pump and CGM use. RESULTS: Among 2380 adults with T1D, 37.4% used insulin pumps and 82.5% used CGM. Insulin pump use was lower among those with income <$80 000 (odds ratio [OR] 0.64 [95% confidence interval 0.50-0.82]), no post-secondary education (OR 0.62 [0.46-0.85]), non-White Race (OR 0.47 [0.30-0.73]) and public insurance (OR 0.47 [0.35-0.62]). CGM use was lower only among those with income <$80 000 (OR 0.61 [0.45-0.83]) and public insurance (OR 0.61 [0.45-0.83]). Odds of insulin pump and CGM use were successively lower with an increasing number of social disadvantage indicators. Insulin pump and CGM use were both associated with lower HbA1c but not severe hypoglycaemia or diabetes hospitalisation. CONCLUSIONS: Social disadvantage is associated with lower uptake of insulin pumps and CGM among Quebec adults with T1D, though public funding partially mitigates disparities in CGM use. Given the benefits and increasing recommendations for automated insulin delivery, strategies to increase the uptake of diabetes technologies among socially disadvantaged individuals are required. PLAIN LANGUAGE SUMMARY: Social disadvantage is linked to lower use of insulin pumps and CGM in adults with T1D in Quebec. Public funding narrows CGM disparities, but broader equity strategies are needed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".