COVID-19 and Compliance with Awareness Programmes/Preventive Measures: A Case Study of Ibadan North Local Government, Oyo State, Nigeria
Bibliographic record
Abstract
The study was carried out to examine the compliance of people to awareness programmes/preventive measures on COVID-19. The study was carried out in Ibadan North Local Government Area of Oyo State with the population being the residents of Ibadan North Local Government area. The study employed a descriptive design of the survey type. A self-designed questionnaire was used to elicit responses from respondents through purposive sampling method via Google form; sent to 400 respondents out of which 200 copies of questionnaire which were filled in a valid form were used for the study. Descriptive statistics of mean and standard deviation with appropriate remarks were used to analyse the research questions while T-test was used to analyze hypotheses 1-3 and ordinary least squares regression was used to analyse hypothesis 4. The study showed that there existed a strong relationship between awareness programmes/preventive measures on COVID-19 and compliance of Ibadan North Local Government area citizens; that there existed a low significant relationship between medium of information and compliance of people to awareness/preventive measures on COVID-19 as indicated in (r = 0.811, P = .000 <0.05) and among others the study revealed that awareness programmes, medium of information and preventive measures had (P = 0.05, R = 0.993, R2 = 0.976, Adjusted R2 = 0.986, F = 4707.2; Sig = 0.000); which showed that these dependent variables had significant relationship with people’s compliance to awareness programmes on COVID-19. Among the recommendations made were that government of Oyo State in particular and Nigeria in general should heighten the process of awareness programmes, provide materials that would help in taking preventive measures to indigent people and educate people on the lookout for verified and certified information from real authoritative sources as opposed to subscribing to fake news.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".