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Record W4205893401 · doi:10.1177/00221465211066855

Dualized Labor Market and Polarized Health: A Longitudinal Perspective on the Association between Precarious Employment and Mental and Physical Health in Germany

2022· article· en· W4205893401 on OpenAlexaff
Timo‐Kolja Pförtner, Holger Pfaff, Frank J. Elgar

Bibliographic record

VenueJournal of Health and Social Behavior · 2022
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsMcGill University
FundersDeutsche Forschungsgemeinschaft
KeywordsMental healthGermanSocioeconomic statusDemographic economicsUnemploymentPovertyPolarization (electrochemistry)Association (psychology)Physical healthPsychologyLabour economicsEconomicsEnvironmental healthMedicineEconomic growthGeographyPopulationPsychiatry

Abstract

fetched live from OpenAlex

This study analyzes the longitudinal association between precarious employment and physical and mental health in a dualized labor market by disaggregating between-employee and within-employee effects and considering mobility in precariousness of employment. Analyses were based on the German Socio-Economic Panel from 2002 to 2018 considering all employees ages 18 to 67 years (n = 38,551). Precariousness of employment was measured as an additive index considering working poverty, nonstandard working time arrangements, perceived job insecurity, and low social rights. Health outcomes were mental and physical health. Random effects models were used and controlled for sociodemographic and socioeconomic variables. Results indicated that the association between precariousness of employment and mental and physical health is mainly based on between-employee differences and that prolonged precariousness of employment or upward or downward mobility are associated with poor health. We found evidence of polarization in health by precariousness of employment within a dualized labor market.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.058
GPT teacher head0.432
Teacher spread0.374 · 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.

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

Citations11
Published2022
Admission routes1
Has abstractyes

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