HIV/AIDS Spread among Rural Farmers in Nigeria: Implication on Village Agricultural Extension Service Delivery
Bibliographic record
Abstract
There is a great public concern on the prevalence and effects of Human Immunes Virus (HIV) and AcquiredImmune Deficiency Syndrome (AIDS) on the rural farmers and agricultural productivity in Nigeria. This studyevaluated the implication of this disease on extension services, using Dekina LGA as its focus. It identified thelevel of HIV/AIDS prevalence by collecting secondary data on rate of HIV/AIDS infection from year 2000 to2005 from medical centers in the study area. The study also examined farmers’ perception on HIV/AIDS usingmean scores from 5 point Likert scale in which, one hundred and sixty contact farmers were interviewed.Farmers had the highest HIV/AIDS infection record with 50.6 percent and 8.19 in year 2001 and 2005respectively. While estimated farmers HIV/AIDS infection by 2010 would be 1,972. Findings also show thatHIV/AIDS has negative effect on farmers health (mean score of 3.88), while 4.13 showed that respondentsfavoured the statement that “stigmatization and the scaring nature of AIDS prevented them from going forHIV/AIDS test. About 20 percent the extension workers claimed that infected farmers negatively affected theirextension work delivery in some ways. This study therefore recommends that every village should be providedwith comprehensive health clinic that would offer free HIV/AIDS treatment while capacity building foragricultural extension agents that will disseminate information on HIV/AIDS to farmers be put in place. Team –work approach among rural development agencies concerned with provision of rural, community social servicesshould also be encouraged.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".