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Record W2544224555 · doi:10.1177/0020731416676233

Employment and Labor Market Results of the SOPHIE Project

2016· article· en· W2544224555 on OpenAlexaff
Mireia Julià, Laia Ollé‐Espluga, Christophe Vanroelen, Deborah De Moortel, Sarah Mousaid, Stig Vinberg, Vanessa Puig‐Barrachina, Esther Sánchez, Carles Muntaner, Lucı́a Artazcoz, Joan Benach

Bibliographic record

VenueInternational Journal of Health Services · 2016
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProsperityEquity (law)InequalityWork (physics)PopulationBusinessSocial securityLabour economicsEconomicsEconomic growthDemographic economicsPolitical scienceEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

This article reports evidence gained by the SOPHIE Project regarding employment and labor market-related policies. In the first step, quality of employment and of precarious and informal employment in Europe were conceptualized and defined. Based on these definitions, we analyzed changes in the prevalence and population distribution of key health-affecting characteristics of employment and work between times of economic prosperity and economic crisis in Europe and investigated their impact on health outcomes. Additionally, we examined the effects of several employment and labor market-related policies on factors affecting health equity, including a specific analysis concerning work-related gender equity policies and case studies in different European countries. Our findings show that there is a need to standardize definitions and indicators of (the quality of) employment conditions and improve information systems. This is challenging given the important differences between and within European countries. In our results, low quality of employment and precarious employment is associated with poor mental health. In order to protect the well-being of workers and reduce work-related health inequalities, policies leading to precarious working and employment conditions need to be suspended. Instead, efforts should be made to improve the security and quality of employment for all workers.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.167

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.041
GPT teacher head0.421
Teacher spread0.380 · 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

Citations9
Published2016
Admission routes1
Has abstractyes

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