Perception and Prevention Practices Relating to Covid 19 Infection Among Elderly in Ogun State, Nigeria
Bibliographic record
Abstract
The risk of contracting COVID-19 and becoming seriously ill increases with age. This study assessed perception and prevention practices relating to COVID 19 infection among the elderly in Ogun State. This study adopted a survey design. One hundred and seventy-five participants were selected using a multi-stage sampling technique. Data were collected using a structured questionnaire, and data collected were analyzed using descriptive and inferential statistics. The majority 142(81.9%) of the participants perceived themselves to be susceptible to COVID-19 infection, with 25(14.3%) perceiving it to be a serious disease. Less than a quarter 40(22.9) of the participants perceived COVID 19 prevention to be highly beneficial while most 115(65.7%) of the participants reported that their levels of barriers to prevention of COVID-19 were high. More than half of 95(54.3%) of the participants had low preventive practices for COVID-19. Participants’ perceived susceptibility to COVID 19 was negatively correlated with their prevention practices (r = -0.15; p = 0.04). In conclusion, the participants had a poor perception of COVID 19 infection and low prevention practices. It is recommended that COVID-19 awareness campaigns should focus on raising more awareness of the risks associated with the COVID 19 infection to make the elderly engage more in preventive behaviours.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".