Compliance with Standard Precaution Practices During the Early Phase of the Covid-19 Pandemic among Nurses in Nigeria
Bibliographic record
Abstract
Background: Nurses serve at the frontline during disease outbreaks. While measures have been adopted to control the rapid spread of the Covid-19 pandemic, little is known about the level of compliance of nurses to standard precaution practices during the early phase of the pandemic. Objective: This study aimed to assess compliance with standard precaution practices (SPPs) among 713 nurses in Nigerian hospitals during the early phase of the pandemic. Method: The study adopted a descriptive cross-sectional design using an anonymous online questionnaire to elicit data from respondents. Data were analysed with the Statistical Package for the Social Sciences, version 25. Chi-square test and multiple regression analyses were also conducted where appropriate. Results: Findings from this study indicated that 448 (62.8%) of the respondents had good knowledge of Covid-19 and 265 (37.2%) had poor knowledge. Also, 529 (74.2%) had good compliance with the practice of standard precautions, and 184 (25.8%) showed non-compliance. A significant association was found between the age of nurses (χ2=14.034 p=0.015), years of experience (χ2=8. 636 p =0.035) and their overall compliance with the practice of standard precautions. Conclusion: During the early phase of the Covid-19 pandemic in Nigeria, although over an average of the nurses had good knowledge of the disease, over one-third had poor knowledge, and one-quarter showed poor compliance with standard precautions. Interventions to improve the knowledge and compliance of health workers during the early phase of disease outbreaks are hereby advocated, as 21.3% had no previous training on Covid-19.
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 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 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".