Unemployment, Informal Work, Precarious Employment, Child Labor, Slavery, and Health Inequalities: Pathways and Mechanisms
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".