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Record W4311137250 · doi:10.1177/07308884221129520

A Forced Vacation? The Stress of Being Temporarily Laid Off During a Pandemic

2022· article· en· W4311137250 on OpenAlexaffabout
Scott Schieman, D. Quan, Philip J. Badawy, Ryu Won Kang

Bibliographic record

VenueWork and Occupations · 2022
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of AlbertaUniversity of Toronto
Fundersnot available
KeywordsContext (archaeology)DistressPandemicMental healthPsychologyMeaning (existential)Affect (linguistics)Coronavirus disease 2019 (COVID-19)Stress (linguistics)Social psychologyClinical psychologyPsychiatryMedicineHistoryPsychotherapistDisease

Abstract

fetched live from OpenAlex

A million Canadian workers suddenly became temporarily laid off (TLO) early into the pandemic. How did this affect mental health? Guided by the Stress Process Model (SPM), we would expect that this job disruption should increase psychological distress. However, given the unique context surrounding the early period of the pandemic, we advance the forced vacation hypothesis, which argues that those who became TLO would—at least initially—report lower levels of distress. To address this puzzle, we use a mixed-methods approach combining a national longitudinal survey dataset and in-depth interviews. Our quantitative analyses reveal that individuals who were TLO had lower distress in April 2020 compared with their peers who continued working. Our interviews uncover several potential explanations for these patterns. The findings provide an elaboration to the SPM as the pandemic context altered the meaning of being TLO, making it feel like a “forced vacation”—at least initially.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.791

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0070.005
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.366
Teacher spread0.327 · 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".

Quick stats

Citations10
Published2022
Admission routes2
Has abstractyes

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