Using a Knowledge and Awareness Survey to Engage and Inform a Community-Based Tuberculosis Intervention among Nomads in Adamawa State, Nigeria
Bibliographic record
Abstract
BACKGROUND: Nomadic populations are frequently isolated and vulnerable to diseases including tuberculosis (TB) and human immunodeficiency virus (HIV) due to limited access to health-related information and services, poverty, and social exclusion. We designed and implemented community-driven and -based outreach for TB and HIV based on the results of a TB knowledge, attitude, and practices (KAP) survey in Adamawa, Nigeria. METHODS: We conducted a cross-sectional study on KAP among nomads using an adapted WHO survey. A TB and HIV community-level active case-finding intervention among nomadic populations was planned and delivered based on the KAP survey results. RESULTS: Among 81 respondents, 26 (32.1%) knew what caused TB. More than 60% reported no health facilities in their community. Radio and healthcare workers were primary sources of information on health. Using community input, we developed and broadcasted radio jingles to sensitize people to TB services. Outreach initiatives led to the verbal screening of 61,891 individuals and 306 were diagnosed with TB. Additionally, 4489 people underwent HIV testing, and 69 were HIV-positive, all of whom were linked to treatment. CONCLUSIONS: The results of KAP surveys can inform the design of evidence-based TB and HIV community-driven and -based case-finding interventions in rural Nigeria among nomadic populations.
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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 | 0.001 | 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) | 0.001 | 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".