Conflicting Voices in HIV/AIDS Education of the South African Youth: School Culture Versus South African Traditional Healers Using Ancestral Worship
Bibliographic record
Abstract
The United Nations Agency of International Development (2013) states that an estimated 24.7 million people are living with HIV in sub-Saharan Africa, nearly 71% of the global total. The 2.9 million are young people aged 15–24 and this generation is living in South Africa. These alarming statistics reveal that various HIV/AIDS prevention strategies have met limited success. The question arises: why? The South African youth face the dilemma that they receive conflicting messages from two opposing sources, each with a strong persuasive pull of its own where HIV/AIDS education is concerned. On one hand, the voice of modern medical science proclaims that the disease is caused by a viral infection that suppresses the victim’s immune response, while on the other hand spiritual voices of African traditional healers offer explanations such as witchcraft or angry ancestors. This article is an attempt to discover whether either or neither of these voices is gaining ground amongst the youth. This article is based on a qualitative phenomenological study conducted at an urban secondary school in Pretoria, South Africa. Empirical findings resulted from the purposive sampling by means of interviews conducted with two focus groups of teachers, three focus groups of grade 12 school learners and one school principal. This was followed by thematic analysis involving the identifying, analysing and reporting patterns (themes) within data. Facts emerging from the research were that conflicting voices are stressful for young people who are subjected to societal pressure to conform and comply with unrealistic expectations. The South African social culture of ancestral worship is very powerful, yet school culture has significant countervailing influence that sheds liberating “light” where gloom of fear, uncertainty and superstition used to prevail. It is critical to note in this regard, for instance, that where HIV/AIDS remedies are concerned, there is no standardised solution for the ‘entire world’ and that a unique situation prevails in the South African social cultural environment where ancestral worship exerts a critically real influence on people’s response to the threat of HIV/AIDS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 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 | 0.014 | 0.015 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".