Diabetes Distress and Pregnancy in Women with Pre-existing Diabetes
Bibliographic record
Abstract
Pre-existing diabetes, type 1 or type 2 diabetes, can be a challenge to manage during pregnancy. Due to the increased fetal and obstetrical risks from hyperglycemia, women are advised to keep blood glucose as close to normal as possible. Diabetes distress is the negative emotional experience of managing diabetes, with prevalence between 20-50% in non-pregnant adults with diabetes. As diabetes distress during pregnancy has not been well studied, the purpose of this study was to use a sequential explanatory mixed methods approach to understand the extent and impact of diabetes distress. This was achieved by first conducting a cross-sectional quantitative study with 76 women pre-existing diabetes. Diabetes distress was measured with the Problem Area in Diabetes (PAID) Scale and a score of 40 or higher indicated high diabetes distress. Women with both types of diabetes and high and low PAID scores were recruited to the second strand, which was an interpretive description qualitative study. Semistructured interviews were conducted with 18 women discuss their experiences of diabetes distress and managing diabetes in pregnancy. In the mixed methods analysis, it was observed that while diabetes distress was seen in 22.4% of women, the majority of women who took part in the qualitative interviews described themes of diabetes distress whether they had a high or low PAID distress score. Current diabetes distress tools are not validated for pregnancy, and qualitative findings indicate that diabetes distress during pregnancy is uniquely defined by worries for the baby. Development of a pregnancy-specific diabetes distress tool for integrated screening during pregnancy would be beneficial to better capture distress rates in this population. The counterpart to the qualitative findings of diabetes distress were findings of resiliency demonstrated by the participants. Further research is needed to better understand appropriate interventions to increase resiliency in pregnancy to mitigate diabetes distress.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".