Examining Diabetes Distress in Pre-existing Diabetes in Pregnancy: Protocol for an Explanatory Sequential Mixed Methods Study
Bibliographic record
Abstract
Diabetes distress has been shown to be highly prevalent in adults living with type 1 and type 2 diabetes with important implications for glycemic control, self-care, and self-management behaviors. Despite considerable focus on self-management and glycemic targets during pregnancy, current literature lacks information on diabetes distress in pregnancy, particularly in women with type 2 diabetes. This article outlines an explanatory sequential mixed methods research protocol to examine diabetes distress during pregnancy in women with pre-existing diabetes. The aims of the study were to: (1) establish the prevalence and correlates of diabetes distress in women attending a diabetes and pregnancy clinic; (2) use this quantitative data to inform development of an interview guide and plan for sampling for telephone interviews; and (3) explore and describe the experiences of diabetes distress during pregnancy. The quantitative strand was a cross-sectional survey of 76 women using self-reported questionnaires to collect demographic and clinical data, and validated tools to assess health variables, including the outcome of interest of diabetes distress using the Problem Area in Diabetes scale. The qualitative strand applied interpretive description methodology to explore the quantitative results using semi-structured qualitative interviews with 18 women to obtain patient perspectives of diabetes distress and experiences of managing diabetes in pregnancy. The explanatory sequential mixed methods research will provide an opportunity to add contextual qualitative experiences from women with pre-existing diabetes during pregnancy to provide a comprehensive picture of diabetes distress. The results will inform further research priorities that protect and promote mental health, psychosocial well-being, and self-management practices for this 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 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 | 0.020 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".