Women's perspectives to improve prenatal care for gestational diabetes: A systematic review and meta‐aggregation of qualitative studies
Bibliographic record
Abstract
INTRODUCTION: In numerous qualitative primary studies, women have identified opportunities to improve prenatal gestational diabetes care. The objective of our systematic review and meta-aggregation was to synthesize patient-guided suggestions for improving prenatal gestational diabetes care that are informed by lived experience of women and their support persons. MATERIAL AND METHODS: This study was registered a priori on PROSPERO (CRD42023394014). Our search strategy was executed in five databases (Medline, PsycInfo, CINAHL, Scopus, and Web of Science). Primary studies that were qualitative, had full texts in English, studied women who have or had gestational diabetes or their support persons, and included experiential accounts on prenatal gestational diabetes care were included. No date restrictions were applied. Studies that were not qualitative, were secondary analyses, included data on only postpartum care, or evaluated an intervention that was not standard care were excluded. Two independent authors used Covidence software to facilitate screening. The outcomes of interest were patient-reported suggestions to improve quality of gestational diabetes care that are informed by women's or their support persons' accounts of the lived experience of gestational diabetes. Meta-aggregation followed by a thematic synthesis approach was used to analyze the qualitative data to identify women's perspectives to improve gestational diabetes care. RESULTS: After duplicate removal, a total of 4761 studies underwent screening and a total of 80 studies were ultimately included. Patient- and support persons-reported suggestions to improve care include timely and comprehensive education around gestational diabetes with active engagement of family members, personalized and tailored counseling, patient-centered care, incorporation of digital or online adjuncts to care, and increasing support for women. CONCLUSIONS: Our systematic review and meta-aggregation identifies several actionable and patient-guided suggestions to improve prenatal gestational diabetes care that are important to consider when embarking on clinical quality improvement.
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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 | 0.130 | 0.233 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.027 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".