Policies and clinical practices relating to the management of gestational diabetes mellitus in the public health sector, South Africa – a qualitative study
Bibliographic record
Abstract
BACKGROUND: Women with a prior gestational diabetes have an increased lifetime risk of developing type 2 diabetes. Although post-partum follow-up for GDM women is essential to prevent progression to type 2 diabetes, it is poorly attended. The need for health systems interventions to support postpartum follow-up for GDM women is evident, but there is little knowledge of actual current practice. The aim of this study was to explore current policies and clinical practices relating to antenatal and post-natal care for women with GDM in South Africa, as well as health sector stakeholders' perspectives on the barriers to -- and opportunities for -- delivering an integrated mother - baby health service that extends beyond the first week post-partum, to the infant's first year of life. METHODS: Following a document review of policy and clinical practice guidelines, in-depth interviews were conducted with 11 key informants who were key policy makers, health service managers and clinicians working in the public health services in South Africa's two major cities (Johannesburg and Cape Town). Data were analysed using qualitative content analysis procedures. RESULTS: The document review and interviews established that it is policy that health services adhere to international guidelines for GDM diagnosis and management, in addition to locally developed guidelines and protocols for clinical practice. All key informants confirmed that lack of postpartum follow-up for GDM women is a significant problem. Health systems barriers include fragmentation of care and the absence of standardised postnatal care for post-GDM women. Key informants also raised patient - related challenges including lack of perceived future risk of developing type 2 diabetes and non-attendance for postpartum follow up, as barriers to postnatal care for GDM women. All participants supported integrated primary health services but cautioned against overloading health workers. CONCLUSION: Although there is alignment between international guidelines, local policy and reported clinical practice in the management of GDM, there is a gap in continuation of care in the postpartum period. Health systems interventions that support and facilitate active follow-up for women with prior GDM are needed if high rates of progression to type 2 diabetes are to be avoided.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".