Diabetes care in remote Australia: the antenatal, postpartum and inter-pregnancy period
Bibliographic record
Abstract
BACKGROUND: Aboriginal and Torres Strait Islander women experience high rates of diabetes in pregnancy (DIP), contributing to health risks for mother and infant, and the intergenerational cycle of diabetes. By enhancing diabetes management during pregnancy, postpartum and the interval between pregnancies, the DIP Partnership aims to improve health outcomes and reduce risks early in the life-course. We describe a mixed methods formative study of health professional's perspectives of antenatal and post-partum diabetes screening and management, including enablers and barriers to care. METHODS: Health professionals involved in providing diabetes care in pregnancy, from a range of health services across the Northern Territory, completed the survey (n = 82) and/or took part in interviews and/or focus groups (n = 62). RESULTS: Qualitative findings highlighted factors influencing the delivery of care as reported by health professionals, including: whose responsibility it is, access to care, the baby is the focus and pre-conception care. The main challenges were related to: disjointed systems and confusion around whose role it is to provide follow-up care beyond six weeks post-partum. Quantitative findings indicated that the majority of health professionals reported confidence in their own skills to manage women in the antenatal period (62%, 40/79) and slightly lower rates of confidence in the postpartum interval (57%, 33/58). CONCLUSION: These findings regarding whose role it is to provide postpartum care, along with opportunities to improve communication pathways and follow up care have informed the design of a complex health intervention to improve health systems and the provision of DIP related care.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".