Healthcare workers’ knowledge, risk perception, safety-relevant practices, and work situation during the COVID-19 pandemic: A quantitative survey from Switzerland
Bibliographic record
Abstract
Objective: To determine the impact of the COVID-19 pandemic on healthcare workers and healthcare students in higher education and to assess their clinical knowledge, media use, risk perception, perception of governmental measures, and adherence to preventive guidelines to provide policymakers with field-based evidence.Methods: This cross-sectional quantitative survey was conducted by two-stage cluster sampling among Swiss healthcare workers, who performed patient care during the first pandemic wave, and who also pursued an education at a university of applied sciences at the same time (a Bachelor’s or Master’s degree in nursing or an executive degree in healthcare). 75 individuals participated between 5th May and 1st June 2020. Their data was analyzed by bivariate hypothesis testing and multiple logistic regression.Results: Considerable levels of task-related and emotional stress were prevalent, accompanied by a large proportion of respondents who did not have sufficient protective materials or necessary decisions in place to effectively protect themselves or others from infection with COVID-19. Knowledge was considerably limited, especially regarding the efficacy of standard hygiene as a preventive measure. The preparation of the government and the healthcare sector was perceived as insufficient.Conclusions: Comprehensive management of infodemic challenges and foresighted development of education, human resources, clinical processes, and protective materials are highly recommended.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".