Perception of healthcare workers towards the government's Coronavirus disease 2019 pandemic response in Ekiti State, Nigeria
Bibliographic record
Abstract
Background: Globally, coronavirus 2019 pandemic has led to severe illnesses, loss of lives, and social disruption in Nigeria. Ekiti State government introduced different strategies, protocols, and standard operating procedures in the control of the pandemic. This study assessed the perception of primary healthcare workers (HCWs) to the measures introduced to combat the coronavirus disease 2019 (COVID-19) pandemic in Ekiti State, Nigeria. Methods: This study was a descriptive cross-sectional study conducted between August and September 2020 among primary HCWs in Ekiti State. A Google survey tool was used to create an online questionnaire which was administered to respondents on social media platform. Analysis was done using STATA SE 12. Descriptive and bivariate analysis were conducted with a level of significance set at P < 0.05. Results: The mean ± standard deviation age of the respondents was 44.2 ± 6.7 years. Almost all (99.4%) of respondents had heard of COVID-19 pandemic while less than three-quarter (67.7%) had been trained on COVID-19. About half (54.6%) and (50.0%), respectively had good knowledge and perception of COVID-19, while three-quarter (75%) had good practice. About half (50.4%) had good perception about government's response toward COVID-19 prevention and protocols. Social and news media and family and friends were significantly associated with respondents' perception toward government' response ( P = 0.000; 0.006 and 0.011) respectively. Similarly, the level of perception and practice of respondents were found to be statistically significant with respondent's perception of government response to COVID-19 ( P = 0.001 and 0.040) respectively. Conclusion: Only about half of the respondents had good knowledge of COVID-19 and positive perception toward government's response to COVID-19 pandemic. Intensification of government's efforts toward the pandemic control in Nigeria is recommended.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".