Vulnerable Workers and COVID-19: Insights from a Survey of Members of the International Commission for Occupational Health
Bibliographic record
Abstract
The COVID-19 pandemic has negatively impacted on the health and wellbeing of populations directly through infection, as well as through serious societal and economic consequences such as unemployment and underemployment. The consequences could be even more severe for those more vulnerable to the disease, such as the elderly and those with underlying health conditions. Indeed, there is evidence that such vulnerable populations are disproportionately affected in terms of both, their health and the socioeconomic impact. The aim of our study was to determine whether occupational health (OH) professionals thought that the COVID-19 pandemic might further disadvantage any particular group(s) of vulnerable workers globally, and if so, which group(s). A cross-sectional study was carried out with a sample of OH professionals by means of an online questionnaire which was shared via email within the ICOH (International Commission for Occupational Health) community. Data was collected over a period of two weeks in May 2020 and 165 responses from 52 countries were received. In this paper, the responses relating to questions about vulnerable workers are reported and discussed. Globally, our responders felt that those in less secure jobs (precarious employment (79%) and informal work (69%)), or unemployed (63%), were the most at risk of further disadvantage from this pandemic. The majority felt that their governments could act to mitigate these effects. There were suggestions of short-term alleviation such as financial and social support, as well as calls for fundamental reviews of the underlying inequalities that leave populations so vulnerable to a crisis such as COVID-19.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".