The Examination and Exploration of Diabetes Distress in Pre-existing Diabetes in Pregnancy: A Mixed-methods Study
Bibliographic record
Abstract
OBJECTIVES: Diabetes distress (DD) has been understudied in the pregnancy population. Pregnancy is known to be a complex, highly stressful time for women with diabetes because of medical risks and the high burden of diabetes management. Our aim in this study was to explain and understand DD in women with pre-existing diabetes in pregnancy. METHODS: An explanatory, sequential mixed-methods study was undertaken. The first strand consisted of a cross-sectional study of 76 women with type 1 and type 2 diabetes. A nested sampling approach was used to re-recruit 18 women back into the second strand for qualitative interviews using an interpretive description approach. RESULTS: DD was measured by the validated Problem Area in Diabetes (PAID) scale. A PAID score of ≥40 was positive for distress. DD prevalence was 22.4% in the cross-sectional cohort and the average PAID score was 27.75 (standard deviation 16.08). In the qualitative strand, women with a range of PAID scores (10.0 to 60.0) were sampled for interviews. The majority of these participants described themes of DD in their interviews. Of the 15 women who described DD thematically, only 6 had positive PAID scores. CONCLUSIONS: Integration of the mixed-methods data underscores important meta-inferences about DD in pregnancy, namely that DD was present to a greater degree than the PAID tool is sensitive to. DD was present qualitatively in most of the qualitative sample, despite interviewing women with a range of PAID scores. Future research on a pregnancy-specific DD scale is 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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".