Integrating gestational diabetes and type 2 diabetes care into primary health care: Lessons from prevention of mother-to-child transmission of HIV in South Africa - A mixed methods study
Bibliographic record
Abstract
BACKGROUND: Implementation of the programmes for the Prevention of Mother to Child Transmission (PMTCT) of Human Immunodeficiency Virus (HIV) into antenatal care over the last three decades could inform implementation of interventions for other health challenges such as gestational diabetes mellitus (GDM). This study assessed PMTCT outcomes, and how GDM screening, care, and type 2 diabetes (T2DM) prevention were integrated into PMTCT in Western Cape (WC), South Africa. METHODS: A convergent mixed methods and triangulation design were used. Content and thematic analysis of PMTCT-related policy documents and of 30 semi-structured interviews with HIV/PMTCT experts, health care workers and women under PMTC diagnosed with GDM complement quantitative longitudinal analysis of PMTCT implementation indicators across the WC for 2012-2017. RESULTS: Provincial PMTCT and Post Natal Care (PNC) documents emphasized the importance of PMTCT, but GDM screening and T2DM prevention were not covered. Data on women with both HIV and GDM were not available and GDM screening was not integrated into PMTCT. Women who attended HIV counselling and testing annually increased at 17.8% (95% CI: 12.9% - 22.0%), while women who delivered under PMTCT increased at 3.1% (95% CI: 0.6% - 5.9%) annually in the WC. All 30 respondents favour integrating GDM screening and T2DM prevention initiatives into PMTCT. CONCLUSION: PMTCT programmes have not yet integrated GDM care. However, Western Cape PMTCT integration experience suggests that antenatal GDM screening and post-partum initiatives for preventing or delaying T2DM can be successfully integrated into PMTCT and primary care.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".