Estimating the burden of United States workers exposed to infection or disease: A key factor in containing risk of COVID-19 infection
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Abstract
No abstract available
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SciScore for 10.1101/2020.03.02.20030288: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Some limitations must be noted. O*NET data were generated from self-reported subjective questionnaires and therefore are subject to bias and misclassification. Respondents may not realize they are exposed to infection or disease at work unless they are in a workplace where these hazards are communicated to them and protective …
SciScore for 10.1101/2020.03.02.20030288: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Some limitations must be noted. O*NET data were generated from self-reported subjective questionnaires and therefore are subject to bias and misclassification. Respondents may not realize they are exposed to infection or disease at work unless they are in a workplace where these hazards are communicated to them and protective equipment is provided (e.g., healthcare sectors) leading to potential differential misclassification across occupational groups. Additionally, information from the O*NET database is applied at the occupation-level, and therefore does not account for within-job exposure variation (25). BLS employment estimates do not capture data on all workers in the United States, including self-employed, undocumented, continent, and domestic workers. These workers may be uniquely at risk to exposures to work because due to limited ability to take time off if they or a family member is ill (26). In Sweden and Norway, higher rates of presenteeism (coming to work when sick) were found among low-income and immigrant workers (27). Access to paid leave, which could ameliorate the financial burden of staying home while sick, varies substantially by occupation, industry, employer, location, and worker sociodemographic profile (e.g., race/ethnicity) (28,29). Workers without access to paid leave have higher rates of presenteeism, and are less likely to receive preventative health services such as getting flu shots (30). Occupational sector also influences rates of presenteeism, ...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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