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Record W1965680795 · doi:10.1136/jech.2003.016634

Psychosocial factors and work related sickness absence among permanent and non-permanent employees

2004· article· en· W1965680795 on OpenAlexaff
David Gimeno, Fernando G. Benavides, Benjamin C. Amick, Joan Benach, José Miguel Martı́nez

Bibliographic record

VenueJournal of Epidemiology & Community Health · 2004
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsInstitute for Work & Health
FundersUniversitat Pompeu Fabra
KeywordsPsychosocialMedicineJob controlJob strainPoisson regressionEuropean unionTemporary workPopulationOccupational safety and healthSick leaveWork (physics)DemographyGerontologyEnvironmental healthPsychiatryPhysical therapy

Abstract

fetched live from OpenAlex

STUDY OBJECTIVE: To examine the association between psychosocial work factors and work related sickness absence among permanent and non-permanent employees by sex. DESIGN: A cross sectional survey conducted in 2000 of a representative sample of the European Union total active population, aged 15 years and older. The independent variables were psychological job demands and job control as measures of psychosocial work environment, and work related sickness absence as the main outcome. Poisson regression models were used to compute sickness absence days' rate ratios. SETTING: 15 countries of the European Union. PARTICIPANTS: A sample of permanent (n = 12 875) and non-permanent (n = 1203) workers from the Third European Survey on Working Conditions. RESULTS: High psychological job demands, low job control, and high strain and passive work were associated with higher work related sickness absence. The risks were more pronounced in non-permanent compared with permanent employees and men compared with women. CONCLUSIONS: This work extends previous research on employment contracts and sickness absence, suggesting different effects depending on psychosocial working conditions and sex.

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)0.012
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.006
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 head0.067
GPT teacher head0.424
Teacher spread0.357 · 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

Citations117
Published2004
Admission routes1
Has abstractyes

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