I'm Not a "Basabasa" Woman: An Explanatory Model of HIV Illness in Ghanaian Women
Bibliographic record
Abstract
Ghana continues to experience an increase in the rate of infection with the human immunodeficiency virus (HIV), with more new infections occurring in women than in men. Prevailing views of health and illness, including indigenous knowledge and traditional beliefs, are an important component of the broad context of disease transmission. Participatory action research was used to explore the explanatory model of HIV illness of 31 seropositive Ghanaian women. Also interviewed were 5 HIV seropositive men, 2 traditional healers, 8 nurses, and 10 professionals, individually and in focus groups, to reflect on the women's comments and the themes emerging from the data. In this article, the women's beliefs about HIV illness will be discussed and their views about the etiology, pathophysiology, symptomology, course of illness, and methods of treatment for their illness will be described. Findings illustrate areas of divergence and convergence between traditional and biomedical explanations of, and treatment for, HIV illness. The necessity for health professionals, particularly nurses, to understand individual and community perceptions about HIV illness is highlighted by the study findings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".