What are the Experiences and Needs of Primary Care Nurses in Caring for Patients With Type 2 Diabetes in a Rural Village in South Africa? An Exploratory Study
Bibliographic record
Abstract
Since 1994, the emphasis in the provision of health services in South Africa has shifted from hospital-based care to a community-based comprehensive primary health care system, especially important in the management of chronic diseases. However, primary health care professional nurses are not well trained to manage chronic conditions like type 2 diabetes. Therefore, this study aimed to explore the experiences and needs of primary care nurses as a basis for the development of a training programme for professional nurses who care for T2D patients. A qualitative descriptive approach was employed, using individual interviews with primary health care nurses caring for T2D patients in the Ga-Dikgale village clinics. Ethical considerations were observed throughout the study and quality supportive measures were employed. Three main themes emerged from the study findings which address the current practices and knowledge of professional nurses related to care provided to diabetes patients, the challenges experienced by professional nurses during the provision of care to diabetes patients on treatment and their training experiences, gaps and needs. A need for continuing education for professional nurses related to the care of patients with diabetes was identified. The results of this study will be used to develop a training programme to improve the knowledge and skills of professional nurses and to improve the quality of care of patients with type 2 diabetes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".