Managing the unknown or the art of preventing SARS-CoV-2 infection in workplaces in a context of evolving science, precarious employment, and communication barriers. A qualitative situational analysis in Quebec and Ontario
Bibliographic record
Abstract
Introduction: The issue of communications in the public space, and in particular, in the workplace, became critical in the early stages of the SARS-CoV-2 pandemic and was exacerbated by the stress of the drastic transformation of the organization of work, the speed with which new information was being made available, and the constant fear of being infected or developing a more severe or even fatal form of the disease. Although effective communication is the key to fighting a pandemic, some business sectors were more vulnerable and affected than others, and the individuals in particular socio-demographic and economic categories were proportionately more affected by the number of infections and hospitalizations, and by the number of deaths. Therefore, the aim of this article is to present data related to issues faced by essential workers interacting with the public and their employers to mitigate the contagion of SARS-CoV-2 (COVID-19) at work. Methods: = 16) on topics related to their work environments in the context of COVID-19 prevention. Results: This article has highlighted some aspects of communication in the workplace essential to preventing COVID-19 outbreaks (e.g., access to information in a context of fast-changing instructions, language proficiency, transparency and confidentiality in the workplace, access to clear guidelines). The impact of poor pre-pandemic working relations on crisis management in the workplace also emerged. Discussion: This study reminds us of the need to develop targeted, tailored messages that, while not providing all the answers, maintain dialog and transparency in workplaces.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".