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Record W3162564387 · doi:10.1371/journal.pone.0255050

The psychological impact of the COVID-19 crisis is higher among young Swiss men with a lower socioeconomic status: Evidence from a cohort study

2021· article· en· W3162564387 on OpenAlexaff
Simon Marmet, Matthias Wicki, Gerhard Gmel, Céline Gachoud, Jean‐Bernard Daeppen, Nicolas Bertholet, Joseph Studer

Bibliographic record

VenuePLoS ONE · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsCentre for Addiction and Mental Health
FundersChina Scholarship CouncilSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsSocioeconomic statusUnemploymentCohort studyCohortMedicineWorkloadDemographyPsychologyDepression (economics)PsychiatryGerontologyPopulationEnvironmental healthEconomic growthEconomics

Abstract

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AIMS: The present study aimed to investigate whether the psychological impact of the COVID-19 crisis varied with regards to young Swiss men's pre-crisis level of education and socioeconomic status and to changes in their work situation due to it. METHODS: A cohort of 2345 young Swiss men (from 21 out of 26 Swiss cantons; mean age = 29) completed survey-based assessments shortly before (April 2019 to February 2020) and early on during the COVID-19 crisis (May to June 2020). Outcomes measured were psychological outcomes before and during the COVID-19 crisis (depression, perceived stress and sleep quality), and the fear, isolation and psychological trauma induced by it. We investigated associations between these outcomes and their predictors: pre-crisis socioeconomic status (relative financial status, difficulty paying bills, level of education), changes in work situation during the crisis (job loss, partial unemployment, working from home, change in workload) and working in contact with potentially infected people, both inside and outside the healthcare sector. For outcomes measured before and during the crisis, the analyses were adjusted for their pre-crisis levels. RESULTS: About 21% of participants changed their employment status (job loss, partial unemployment or lost money if self-employed) and more than 40% worked predominantly from home during the COVID-19 crisis. Participants with a lower relative socioeconomic status already before the crisis experienced a higher psychological impact due to the COVID-19 crisis, compared to participants with an average socioeconomic status (major depression (b = 0.12 [0.03, 0.22]), perceived stress (b = 0.15 [0.05, 0.25]), psychological trauma (b = 0.15 [0.04, 0.26]), fear (b = 0.20 [0.10, 0.30]) and isolation (b = 0.19 [0.08, 0.29])). A higher impact was also felt by participants who lost their job due to the COVID-19 crisis, the partially unemployed, those with an increased workload or those who worked mainly from home (e.g. depression b = 0.25 [0.16, 0.34] for those working 90%+ at home, compared to those not working at home). CONCLUSIONS: Even in a country like Switzerland, with relatively high social security benefits and universal healthcare, the COVID-19 crisis had a considerable psychological impact, especially among those with a lower socioeconomic status and those who experienced deteriorations in their work situation due to the COVID-19 crisis. Supporting these populations during the crisis may help to prevent an amplification of inequalities in mental health and social status. Such support could help to lower the overall impact of the crisis on the mental well-being of Switzerland's population.

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 score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.119
GPT teacher head0.418
Teacher spread0.298 · 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

Citations35
Published2021
Admission routes1
Has abstractyes

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