What Are the Experiences and Training Needs of Home-Based Carers in Dealing With Diabetes in a Rural Village in South Africa? An Explorative Study
Bibliographic record
Abstract
Type 2 Diabetes Mellitus is a difficult chronic condition associated with morbidity, mortality and loss of quality of life. In Sub-Saharan African countries, HBC (Home-Based Carers) play an important role in the care of people diagnosed with chronic illnesses like diabetes. However, home-based carers seem to lack knowledge to care of people with diabetes because they have not been not formally trained. The aim of this study is to explore and describe the practices, knowledge and learning needs of Home-Based Carers (HBCs) of people with diabetes. A qualitative explorative approach was taken, holding interviews with 15 HBCs at the 4 clinics in the Ga-Dikgale village. The purposive sampling method was used to select participants for this study. Four themes are described: activities performed by HBCs during the care of diabetes patients, existing structures and sources of information for HBCs on the management of diabetic patients, challenges experienced by HBCs during the provision of care to diabetes patients and the learning needs of HBCs, based on how they want their training to be organised. HBCs execute various activities during the care of PWD (patients with diabetes) including providing nutritional advice, medication support, helping with household chores, accompanying patients to healthcare services and dressing their wounds. However, they lack knowledge of issues related to the care of PWD, which makes their role very difficult and challenging.
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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 | 0.004 | 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 | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".