On Curtailing the Spread of SARS-CoV-2 Pandemic Among Religious Followers in Nigeria: An Empirical Study
Bibliographic record
Abstract
Controlling religious gathering during the time of rapid spread of COVID-19 pandemic becomes a serious problem faced by governments at all levels. In fact, all other areas of human endeavor where large gatherings could take place in Nigeria such as club houses, beer parlors, ceremonial gatherings, schools and institutions, market places including kiosks and shops, airports and motor parks, commercial and other money-deposit banks, private and public offices, etcetera were kept under perfect control by government except the religious centers. The global trend of COVID-19 pandemic has become more worrisome as the incidence keeps increasing every day. Curtailing its spread among the human race should be a responsibility to be performed by every individual not only in Nigeria but also in the whole world. However, the influence of religious organizations can never be overlooked in dealing with societal attitude towards curtailing the outbreak of the pandemic. Therefore, the data used for this study were sourced via an online survey designed from Google account with a link. Having expunged incomplete information from the responses received, two thousand, three hundred and sixty useful ones were analyzed using R software. Descriptive statistics such as frequency, percentages and graphics were employed alongside chi-squared test as statistical tools. Empirically, results reveal that it’s the responsibility of everyone to encourage curtailing the spread of COVID-19 pandemic. Also, it’s established that religious followers do not fear the pandemic; they only obeyed government’s lock-down directive. Though majority of the citizens (84%) consider this policy as punishment, people prefer worshipping their God (gods) at home, if possible from home while engaging with their daily commercial activities. Hence, the study also reveals that most of the religious teachings during the pandemic were meant to publicly sensitize citizens about how dangerous COVID-19 is. This was supported by approximately 73% of the respondents. We therefore recommend that representatives of religious leaders and organizations should be involved during policy formulation, especially health-related policies as evidenced from the study.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".