Integrating Gestational Diabetes Screening and Care and Type 2 Diabetes Mellitus Prevention After GDM Into Community Based Primary Health Care in South Africa-Mixed Method Study
Bibliographic record
Abstract
Background: Despite high gestational diabetes mellitus (GDM) prevalence in South Africa (9.1% in 2018), its screening and management are not well integrated into routine primary health care and poorly linked to post-GDM prevention of type 2 diabetes mellitus (T2DM) in South Africa's fragmented health system. This study explored women's, health care providers' and experts' experiences and perspectives on current and potential integration of GDM screening and prevention of T2DM post-GDM within routine, community-based primary health care (PHC) services in South Africa. Methods: This study drew on the Behaviour Change Wheel (BCW) framework and used a mixed method, sequential exploratory design for data collection, analysis and interpretation. Individual semi-structured interviews were conducted with key informants (n = 5) from both national and provincial levels and health care providers (n = 18) in the public health system of the Western Cape Province. Additionally, focus group discussions (FGDs) with Community Health Workers (CHWs n = 15) working with clinics in the Western Cape province. A further four FGDs and brief individual exit interviews were conducted with women with GDM (n = 35) followed-up at a tertiary hospital: Groote Schuur Hospital (GSH). Data collection with women diagnosed and treated for GDM happened between March and August 2018.Thematic analysis was the primary analytical method with some content analysis as appropriate. Statistical analysis of quantitative data from the 35 exit interview questionnaires was conducted, and correlation with qualitative variables assessed using Cramér's V coefficient. Results: Shortage of trained staff, ill-equipped clinics, socio-economic barriers and lack of knowledge were the major reported barriers to successful integration of GDM screening and postnatal T2DM prevention. Only 43% of women reported receiving advice about all four recommendations to improve GDM and decrease T2DM risk (improve diet, reduce sugar intake, physical exercise and regularly take medication). All participants supported integrating services within routine, community-based PHC to universally screen for GDM and to prevent or delay development of T2DM after GDM. Conclusion: GDM screening and post-GDM prevention of T2DM are poorly integrated into PHC services in South Africa. Integration is desired by stakeholders (patients and providers) and may be feasible if PHC resource, training constraints and women's socio-economic barriers are addressed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".