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Record W2115493414 · doi:10.2190/hs.40.2.h

Unemployment, Informal Work, Precarious Employment, Child Labor, Slavery, and Health Inequalities: Pathways and Mechanisms

2010· article· en· W2115493414 on OpenAlexaff
Carles Muntañer, Orielle Solar, Christophe Vanroelen, José Miguel Martı́nez, Vilma Sousa Santana, Antía Castedo, Il‐Ho Kim, Joan Benach

Bibliographic record

VenueInternational Journal of Health Services · 2010
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsUnemploymentInequalitySocial inequalityContext (archaeology)Demographic economicsInformal sectorLabour economicsPsychosocialWelfare stateWelfarePrecarious workEconomicsWork (physics)PsychologyEconomic growthPolitical sciencePoliticsGeography

Abstract

fetched live from OpenAlex

The study explores the pathways and mechanisms of the relation between employment conditions and health inequalities. A significant amount of published research has proved that workers in several risky types of labor--precarious employment, unemployment, informal labor, child and bonded labor--are exposed to behavioral, psychosocial, and physio-pathological pathways leading to physical and mental health problems. Other pathways, linking employment to health inequalities, are closely connected to hazardous working conditions (material and social deprivation, lack of social protection, and job insecurity), excessive demands, and unattainable work effort, with little power and few rewards (in salaries, fringe benefits, or job stability). Differences across countries in the social contexts and types of jobs result in varying pathways, but the general conceptual model suggests that formal and informal power relations between employees and employers can determine health conditions. In addition, welfare state regimes (unionization and employment protection) can increase or decrease the risk of mortality, morbidity, and occupational injury. In a multilevel context, however, these micro- and macro-level pathways have yet to be fully studied, especially in middle- and low-income countries. The authors recommend some future areas of study on the pathways leading to employment-related health inequalities, using worldwide standard definitions of the different forms of labor, authentic data, and a theoretical framework.

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)0.002
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 score0.201
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.039
GPT teacher head0.382
Teacher spread0.343 · 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

Citations151
Published2010
Admission routes1
Has abstractyes

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