Assessing knowledge, attitude, practice and training related to COVID-19: a cross-sectional survey of frontline healthcare workers in Nigeria
Bibliographic record
Abstract
OBJECTIVES: Healthcare workers (HCWs) are at the frontline of efforts to treat those affected by COVID-19 and prevent its continued spread. This study seeks to assess knowledge, attitude and practice (KAP) as well as training needs and preferences related to COVID-19 among frontline HCWs in Nigeria. SETTING: A cross-sectional survey was carried out among 1852 HCWs in primary, secondary and tertiary care settings across Nigeria using a 33-item questionnaire. PARTICIPANTS: Respondents included doctors, nurses, pharmacy and clinical laboratory professionals who have direct clinical contact with patients at the various healthcare settings. ANALYSIS: Exploratory factor analysis (EFA) was used to establish independent factors related to COVID-19 KAP. Analysis of variance was used to identify any differences in the factors among different categories of HCWs. RESULTS: EFA identified four factors: safety and prevention (factor 1), practice and knowledge (factor 2), control and mitigation (factor 3) and national perceptions (factor 4). Significant group differences were found on three factors: Factor 1 (F(1,1655)=5.79, p=0.0006), factor 3 (F(1,1633)=12.9, p<0.0.0001) and factor 4 (F(1,1655)=7.31, p<0.0001) with doctors scoring higher on these three factors when compared with nurses, pharmaceutical workers and medical laboratory scientist. The most endorsed training need was how to reorganise the workplace to prevent spread of COVID-19. This was chosen by 61.8% of medical laboratory professionals, 55.6% of doctors, 51.7% of nurses and 51.6% of pharmaceutical health workers. The most preferred modes of training were webinars and conferences. CONCLUSION: There were substantial differences in KAP regarding the COVID-19 pandemic among various categories of frontline HCWs surveyed. There were also group differences on COVID-19 training needs and preferences. Tailored health education and training aimed at enhancing and updating COVID-19 KAP are needed, particularly among non-physician HCWs.
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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 | 0.000 | 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) | 0.002 | 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".