Occupational Health and Safety Measures in Healthcare Settings during COVID-19: Strategies for Protecting Staff, Patients and Visitors
Bibliographic record
Abstract
The COVID-19 (SARS-CoV-2) pandemic has profoundly impacted almost every aspect of healthcare systems worldwide, placing the health and safety of frontline healthcare workers at risk, and it still continues to remain an important public health challenge. Several hospitals have put in place strategies to manage space, staff, and supplies in order to continue to deliver optimum care to patients while at the same time protecting the health and safety of staff and patients. However, the emergence of the second and third waves of the virus with the influx of new cases continue to add an additional level of complexity to the already challenging situation of containing the spread and lowering the rate of transmission, thus pushing healthcare systems to the limit. In this narrative review paper, we describe various strategies including administrative controls, environmental controls, and use of personal protective equipment, implemented by occupational health and safety departments for the protection of healthcare workers, patients, and visitors from SARS-CoV-2 virus infection. The protection and safeguard of the health and safety of healthcare workers and patients through the implementation of effective infection control measures, adequate management of possible outbreaks and minimization of the risk of nosocomial transmission is an important and effective strategy of SARS-CoV-2 pandemic management in any healthcare facility. High quality patient care hinges on ensuring that the care providers are well protected and supported so they can provide the best quality of care to their patients.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".