How Do Macro-Level Contexts and Policies Affect the Employment Chances of Chronically Ill and Disabled People? Part II: The Impact of Active and Passive Labor Market Policies
Bibliographic record
Abstract
The authors investigate three hypotheses on the influence of labor market deregulation, decommodification, and investment in active labor market policies on the employment of chronically ill and disabled people. The study explores the interaction between employment, chronic illness, and educational level for men and women in Canada, Denmark, Norway, Sweden, and the United Kingdom, countries with advanced social welfare systems and universal health care but with varying types of active and passive labor market policies. People with chronic illness were found to fare better in employment terms in the Nordic countries than in Canada or the United Kingdom. Their employment chances also varied by educational level and country. The employment impact of having both chronic illness and low education was not just additive but synergistic. This amplification was strongest for British men and women, Norwegian men, and Danish women. Hypotheses on the disincentive effects of tighter employment regulation or more generous welfare benefits were not supported. The hypothesis that greater investments in active labor market policies may improve the employment of chronically ill people was partially supported. Attention must be paid to the differential impact of macro-level policies on the labor market participation of chronically ill and disabled people with low education, a group facing multiple barriers to gaining employment.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".