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Record W7071665096

The Short-Term Economic Consequences of COVID-19: Occupation Tasks and Mental Health in Canada

2020· report· en· W7071665096 on OpenAlexaboutno aff

Bibliographic record

VenueCarleton University's Institutional Repository (MacOdrum Library, Carleton University) · 2020
Typereport
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicWork (physics)Mental healthPerspective (graphical)UnemploymentJob lossOccupational safety and healthCoronavirus disease 2019 (COVID-19)
DOInot available

Abstract

fetched live from OpenAlex

In this paper, we document the short-term impact of COVID-19 on labour market outcomes in Canada. Following a pre-analysis plan, we investigate the negative impact of the pandemic on unemployment, labour force participation, hours and wages in Canada. We find that COVID-19 had drastic negative effects on labour market outcomes, with the largest effects for younger, not married, and less educated workers. We investigate whether the economic consequences of this pandemic were larger for certain occupations. We then built indices for whether (1) workers are relatively more exposed to disease, (2) work with proximity to coworkers, (3) are essential workers, and (4) can easily work remotely. Our estimates suggest that the impact of the pandemic was significantly more severe for workers more exposed to disease and workers that work in proximity to coworkers, while the effects are significantly less severe for essential workers and workers that can work remotely. Last, we rely on a unique survey, the Canadian Perspective Survey, and show that
\nreported mental health is significantly lower among the most affected workers during the pandemic. We also find that those who were absent form work because of COVID-19 are more concerned with meeting their financial obligations and with losing their job than those who remain working outside of home, while those who transition from working outside the home to from home are not as concerned with job loss.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.573
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0020.003
Scholarly communication0.0000.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.233
Teacher spread0.213 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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