How Do Macro-Level Contexts and Policies Affect the Employment Chances of Chronically Ill and Disabled People? Part I: The Impact of Recession and Deindustrialization
Bibliographic record
Abstract
Low employment rates of chronically ill and disabled people are of serious concern. Being out of work increases the risk of poverty and social exclusion, which may further damage the health of these groups, exacerbating health inequalities. Macro-level policies have a potentially tremendous impact on their employment chances, and these influences urgently need to be understood as the current economic crisis intensifies. In Part I of this two-part study, the authors examine employment trends for people who report a chronic illness or disability, by gender and educational level, in Canada, Denmark, Norway, Sweden, and the United Kingdom in the context of economic booms and busts and deindustrialization. People with the double burden of chronic illness and low education have become increasingly marginalized from the labor market. Deindustrialization may have played a part in this process. In addition, periods of high unemployment have sparked a downward trend in employment for already marginalized groups who did not feel the benefits when the economy improved. Norway and Sweden have been better able to protect the employment of these groups than the United Kingdom and Canada. These contextual differences suggest that other macro-level factors, such as active and passive labor market polices, may be important, as examined in part II.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".