Ebola Outbreak in Nigeria: Increasing Ebola Knowledge of Volunteer Health Advisors
Bibliographic record
Abstract
In many low-income countries, volunteer health advisors (VHAs) play an important role in disseminating information, especially in rural or hard-to-reach locations. When the world's largest outbreak of Ebola virus disease (EVD) occurred in 2014, a majority of cases were concentrated in the West African countries of Guinea, Liberia, and Sierra Leone. Twenty cases were reported in Nigeria initially and there was a need to rapidly disseminate factual information on Ebola virus. In southeast Nigeria, a group of VHAs was being used to implement the Healthy Beginning Initiative [HBI], a congregation based intervention to increase HIV testing among pregnant women and their male partners. The purpose of this study was to assess the baseline and post EVD training knowledge of VHAs during the outbreak in Nigeria. In September 2014, 59 VHAs attending a HBI training workshop in the Enugu State of Nigeria participated in an Ebola awareness training session. Participants completed a 10-item single-answer questionnaire that assessed knowledge of Ebola epidemiology, symptoms, transmission, prevention practices, treatment and survival prior to the Ebola awareness training. After the training, the VHAs repeated the questionnaire. Answers to pre and post questionnaires were analyzed using paired t-tests. Multiple linear regression was used to examine the relationship between pre and post total questionnaire scores and age, education, current location and employment. The average pre-test score was 7.3 and average post-test score was 7.8 which was a significant difference (t=-2.5, p=0.01). Prior to the training, there was a significant difference in Ebola knowledge based on the VHAs education only (p<0.01). After training, education was no longer significant for Ebola knowledge. Existing community health programs can be used as a platform to train VHAs in times of epidemics for quick dissemination of vital health information in areas lacking adequate health infrastructure and personnel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".