The Socio-Economic Effects of HIV/AIDS in Sub-Saharan Africa: The Nigerian Case
Bibliographic record
Abstract
Since the discovery of the Human Immunodeficiency Virus (HIV) and Acquired Immune Deficiency Syndrome (AIDS) in the early 80s on the continent of Africa and Nigeria in 1986, the disease has turned out to be the most devastating and destructive in contemporary African societies. It has a serious impact on human resources and other aspects of societal development. The paper has examined some of the social and economic effects in Nigeria, using secondary data. It pointed out the impact of the disease on the population and loss of lives among the youth in their productive and reproductive ages, which reduces the labor supply which, in turn affects the overall economic output at micro and macro levels. The effect is glaring on the family that bears the cost of medical care and other expenses in addition to the suffering from stigma associated with AIDS. The financial burden of the family is responsible for the reduced care and consumption pattern in particular for women and children. The infected persons who remain alive but lose their jobs continue to face the problem of settling medical bills. The children of the dead who become orphans lose parental care and the required support for education and other welfare services. Among others, the paper recommends that the government and other stake-holders should put more efforts on the prevention of new infections and initiate welfare programs to address the problems of the infected and the immediate members of their families, in particular women and children. Keywords: HIV/AIDS, Social Effects, Economic Effects, Stigma, Discrimination
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".