Supporting Diabetes Self-Management in Pregnancies Complicated by Type 1 and Type 2 Diabetes
Bibliographic record
Abstract
The occurrence of pre-existing type 1 and type 2 diabetes in pregnancy has been on the rise, parallel with the current “diabetes pandemic” (Albrecht et al., 2010; Coton et al., 2016; Feig et al., 2014; The Lancet, 2011). Currently, pre-existing diabetes affects up to 2.4% of pregnancies around the world (Deputy et al., 2018; Fadl & Simmons, 2016; Lopez-de-Andres et al., 2020; Tutino et al., 2014; Wahabi et al., 2017). Importantly, women with type 1 and type 2 diabetes are at a high risk of experiencing perinatal complications. Perinatal complications range from neonatal hypoglycemia to fetal and infant death (Feig et al., 2014; Kishida et al., 1989). The risk of complications is related to maternal glycemia; maintaining tight glycemic control within the recommended ranges for pregnancy is associated with a reduced risk of adverse outcomes (Feig et al., 2018; Inkster et al., 2006; Tennant et al., 2014). To achieve this, women experience a heavy burden of diabetes self-management during pregnancy. Little is known regarding the predictors of glycemic control during pregnancies complicated by type 1 and type 2 diabetes and their relationship with self-management factors, such as self-efficacy. Furthermore, the impact of these factors in combination with women’s pregnancy experiences has not been explored. The objective of this thesis was to explore how self-management and support experiences help explain glycemic control among women with pre-existing diabetes in pregnancy. There were four overarching questions: (a) What are the predictors of glycemic control during pregnancy among women with pre-existing diabetes? (b) What is the experience of managing diabetes during pregnancy? (c) What are the diabetes self-management education and support needs during pregnancy among women with pre-existing diabetes? (d) How do the self-management and support experiences of women with pre-existing diabetes in pregnancy help explain their glycemic control? The results of this sandwich thesis aim to answer these questions. The findings showed that women achieved tight glycemic control during pregnancy as they were motivated by the worry of complications for their unborn child. Fear related to complications, feeling unsupported by the healthcare team and a lack of connection with other mothers with diabetes contributed to compromised mental health. Future research should explore the development, implementation and evaluation of interventions to increase mental health support, peer support and support from the healthcare team for this vulnerable population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".