Safety management practices among Saudi healthcare professionals during pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has greatly impacted organizational processes and activities. Unlike previous pandemics, COVID-19 has affected everyone directly or indirectly. To protect employees from the virus and associated risks, organizations have focused on developing occupational health and safety management systems. While safety policies and practices were already in place before the pandemic, the emergence of new physical and psychological risks has led organizations to amend their safety and health management systems. Governments have introduced health containment measures such as social distancing, working in shifts, and mandatory quarantine to enhance safety for people worldwide. Employers have also introduced safety measures to build confidence in implementation. These safety management practices have influenced employees' behaviors during the pandemic, and this study aims to examine their impact. Specifically, the study aims to determine the impact of management practices on the behavior of healthcare employees regarding their safety in a threatening environment. Additionally, the study seeks to investigate the indirect influence of management practices on employees' behavior through perceived risks and efficacy. It is important to note that there has been a lack of research on the impact of COVID-19 on healthcare workers in Saudi Arabia. This study found that management commitment did not directly influence employee safety behavior. However, management commitment towards workplace safety practices had a significant and direct influence on healthcare employees' perceived risk associated with COVID-19 and their efficacy. Consequently, management commitment was found to indirectly influence employee safety behavior through efficacy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".