Trends and Self-Management Predictors of Glycemic Control During Pregnancy in Women With Preexisting Type 1 or Type 2 Diabetes: A Cohort Study
Bibliographic record
Abstract
Background: Because much of diabetes management during pregnancy occurs at home, self-management factors such as self-efficacy, self-care activities, and care satisfaction may affect glycemia. Our objective was to explore trends in glycemic control during pregnancy in women with type 1 or type 2 diabetes; assess self-efficacy, self-care, and care satisfaction; and examine these factors as predictors of glycemic control. Methods: We conducted a cohort study from April 2014 to November 2019 at a tertiary center in Ontario, Canada. Self-efficacy, self-care, care satisfaction, and A1C were measured three times during pregnancy (T1, T2, and T3). Linear mixed-effects modeling explored trends in A1C and examined self-efficacy, self-care, and care satisfaction as predictors of A1C. Results: We recruited 111 women (55 with type 1 diabetes and 56 with type 2 diabetes). Mean A1C significantly decreased by 1.09% (95% CI -1.38 to -0.79) from T1 to T2 and by 1.14% (95% CI -1.43 to -0.86) from T1 to T3. Self-efficacy significantly predicted glycemic control for women with type 2 diabetes and was associated with a mean change in A1C of -0.22% (95% CI -0.42 to -0.02) per unit increase in scale. The exercise subscore of self-care significantly predicted glycemic control for women with type 1 diabetes and was associated with a mean change in A1C of -0.11% (95% CI -0.22 to -0.01) per unit increase in scale. Conclusion: Self-efficacy significantly predicted A1C during pregnancy in a cohort of women with preexisting diabetes in Ontario, Canada. Future research will continue to explore the self-management needs and challenges in women with preexisting diabetes in pregnancy.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".