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Exposure to COVID-19: is there a disproportionate burden on low-paid jobs in France?

2020· article· en· W3167361639 on OpenAlexaboutno aff
Émilie Counil, Narges Ghoroubi, Myriam Khlat

Bibliographic record

VenueISEE Conference Abstracts · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsSalaryQuarter (Canadian coin)Personal protective equipmentEnvironmental healthCoronavirus disease 2019 (COVID-19)MedicineBusinessSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Occupational safety and healthDemographic economicsEconomicsGeographyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Although a majority of COVID-19 victims are among the elderly, workers holding low-paid jobs in the essential service sectors and those who are more likely to trade their health for economic reasons may be particularly exposed. Our aim is to gather existing data suggesting a disproportionate burden of the epidemic on the lower income categories.We focus on the situation of France, which has been hard hit. Workers highly exposed to the risk of COVID-19 infection are those who routinely have close face-to-face contacts with the public/colleagues, and/or exposure to infectious agents. Prior knowledge on usual working conditions can help us highlight at-risk occupations and related risks outside the work environment during the epidemic. We analysed national data on working conditions (CT2013), exposure to occupational hazards (SUMER2017) and a flash survey conducted during the lockdown.Before the lockdown (mid-March 2020), at least 8.8 million of workers were highly exposed to Covid-19 in France. There were however sharp disparities across occupational groups. As high as 41% of the bottom quarter of earners belonged to the highly exposed group, as opposed to 12% of the top quarter of earners. Apart from health care workers and first responders, other frontline workers with low-pay such as cleaners, personal aids and cashiers are among the most exposed. The situation has yet changed during the lockdown, with teleworking, reduced hours/layoffs, and type and timing of protective measures taken by employers. Lower salary workers have been highly exposed to the risk of COVID-19 infection. They may carry a heavy health burden related to the current crisis, especially when not sufficiently protected. Their occupational risks are further compounded by their transportation and housing conditions, along with comorbidities and access to healthcare. This lays ground to greater spread and severity of the disease among working-age and older working-class adults.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score0.273

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.072
GPT teacher head0.285
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations2
Published2020
Admission routes1
Has abstractyes

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