'Better to die of disease than die of hunger': the experience of Igwes (traditional rulers) in the fight against the COVID-19 pandemic in rural South-east Nigeria
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has had serious health and socioeconomic impacts on people all over the world. It was expected that Africa would be the hardest hit; consequently, Nigeria and other African nations worked with non-government organisations to institute a framework for controlling the spread of the disease and the resultant economic woes. The measures, however, largely focused on urban centres, whereas the spread of the virus and the disease transcended imported urban cases to spread through the rural community. This study explored the experiences of traditional rulers, who are closest to rural people, in the fight against COVID-19. METHODS: A qualitative research design was adopted and data were collected from eight Nigerian traditional rulers through interviews. The collected data were coded inductively using NVivo v12 and were then analysed thematically. RESULTS: Findings showed that the traditional rulers adopted measures such as the use of town criers to raise awareness among rural people about COVID-19. Findings also revealed that the protection measures led to increased economic hardship for rural people in Nigeria. Doubt about the existence of the virus and widespread poverty were found to be the major hindrances in the fight against the pandemic. CONCLUSION: It is recommended that traditional rulers collaborate with the government to make free protective equipment available for poor rural people, and collaborate with youths and religious leaders to properly fight the 'infodemic' through continuous community education and awareness-raising.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| 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".