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Record W3040819108

COVID-19, Occupation Tasks and Mental Health in Canada

2020· article· en· W3040819108 on OpenAlexaboutno aff
Louis‐Philippe Beland, Abel Brodeur, Derek Mikola, Taylor Wright

Bibliographic record

VenueCarleton Economic Papers · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthPandemicCoronavirus disease 2019 (COVID-19)Work (physics)Demographic economicsPerspective (graphical)PsychologySurvey data collectionBusinessMedicineGerontologyLabour economicsDiseaseEconomicsPsychiatryInfectious disease (medical specialty)
DOInot available

Abstract

fetched live from OpenAlex

In this paper, we study the effect of COVID-19 on the labour market and reported mental health of Canadians. We document that COVID-19 had drastic impact on labour market outcomes in Canada, with the largest effects for younger and less educated workers. To further understand the effect of the pandemic on the labour market, we build indexes for whether (1) workers are relatively more exposed to disease, (2) work in proximity to coworkers, (3) are essential workers, and (4) can easily work remotely. Our estimates suggest that the impact of COVID- 19 was significantly more severe for workers more exposed to disease and workers that work in proximity to coworkers, while the effects are less severe for essential workers and workers that can work remotely. Last, using the Canadian Perspective Survey, we find that reported mental health is significantly lower among the most affected workers. 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 continue working outside their home, while those who transition from working outside the home to their home are not as concerned with job loss. Our analysis points to the individuals the most affected by COVID-19.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.827
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

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

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 head0.037
GPT teacher head0.257
Teacher spread0.220 · 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 teacher head, 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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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