Health Knowledge and Non-Pharmaceutical Interventions During the COVID-19 Pandemic in Africa
Bibliographic record
Abstract
Providing health information is a non-pharmaceutical intervention designed to reduce disease transmission and infection risk by encouraging behavior change.But does knowledge change behavior?We test whether coronavirus health knowledge promotes protective risk mitigation behaviors early in the COVID-19 pandemic across four African countries (Ghana, Malawi, Sierra Leone, and Tanzania).Despite reputations for weak health sectors and low average levels of education, health knowledge of the symptoms and transmission mechanisms was high in all countries in the two months after the virus entered the country.Higher knowledge is associated with increased protective measures that would likely lower disease risk with one exceptionknowledge is inversely correlated with social distancing.Respondents largely adhered to mask mandates and lockdowns, but continued coming into contact with others at small, informal gatherings, gatherings not affected by mandates.Knowledge alone appears unlikely to reduce all risky activities, especially gatherings within other people's homes.Even early in the pandemic income loss or stress were commonly reported.Our results suggest that early and consistent government provision of health information, likely reduced the severity of the pandemic in Africa but was not a panacea.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".