Benefits of Paid Sick Leave During the COVID-19 Pandemic
Bibliographic record
Abstract
Multiple jurisdictions have adopted or adapted paid sick leave policies to reduce the likelihood of employees infected with SARS-CoV-2 presenting to work, which can lead to the spread of infection in workplaces. During the COVID-19 pandemic, paid sick leave has been associated with an increased likelihood of workers staying at home when symptomatic. Paid sick leave can support essential workers in following public health measures. This includes paid time off for essential workers when they are sick, have been exposed, need to self-isolate, need time off to get tested, when it is their turn to get vaccinated, and when their workplace closes due to an outbreak. In the United States, the introduction of a temporary paid sick leave, resulted in an estimated 50% reduction in the number of COVID-19 cases per state per day. The existing Canada Recovery Sickness Benefit (CRSB) cannot financially protect essential workers in following all public health measures, places the administrative burden of applying for the benefit on essential workers, and neither provides sufficient, nor timely payments. Table 1 lists the characteristics of a model paid sick leave program as compared with the CRSB. Implementation of the model program should be done in a way that is easy to navigate and quick for employers.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".