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Record W1999011006 · doi:10.1002/ajim.20974

Incidence of work and non‐work related disability claims in Brazil

2011· article· en· W1999011006 on OpenAlexaff
Anadergh Barbosa‐Branco, Wagner H. Souza, Ivan Steenstra

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2011
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsInstitute for Work & Health
Fundersnot available
KeywordsMedicineIncidence (geometry)Work (physics)Sick leaveAbsenteeismDemographyPopulationTemporary workOccupational safety and healthEnvironmental healthOccupational medicineGerontologyPhysical therapyOccupational exposurePsychologyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Sickness benefit claims are an important economic burden to society. This study aims to determine the incidence of sickness benefit claims in Brazil in 2008, exploring the role of economic activity. METHODS: Population-based study on sickness claims lasting longer than 15 days of sickness absence granted to private sector employees. Data on gender, age, economic activity, diagnosis, and work-relatedness were collected. RESULTS: The annual incidence of sickness benefits was 421.8/10,000 jobs, 435.4 for males and 452.0 for females. There were 3.5 times more non-work-related than work-related claims. The main diagnoses were injuries, musculoskeletal disorders, and mental disorders. Rates increased with age up to 59 years. Economic activity 37-Sewage had the highest incidence of non-work-related and work-related claims. CONCLUSION: The incidence of sickness benefits is higher among female and older workers. Economic activities show great variability of sickness benefit rates, work-relatedness, diagnostic categories, and gender.

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)0.002
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 score0.104
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.384
Teacher spread0.326 · 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

Citations38
Published2011
Admission routes1
Has abstractyes

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