The importance of government policies in reducing employment related health inequalities
Bibliographic record
Abstract
The current economic recession has caused striking levels of unemployment, underemployment, and job insecurity globally. The International Labour Organization (ILO) estimated that the number of unemployed people was 212 million in 2009, and it projects the global unemployment rate in 2010 to be 6.5%, with a confidence interval ranging from 6.1% to 7%. In rich countries in the Organization for Economic Co-operation and Development more than 57 million people, or 10%, are unemployed in 2010, the current unemployment rate in Spain is 20%, and in the United States the rate is around 10% using conservative estimates. The ILO has predicted that the impact of the economic crisis on vulnerable employment is likely to have increased the number of working poor—those living on $1.25 (£0.80; €0.90) a day—by 215 million workers between 2008 and 2009, and that in 2009 there were between 1.48 and 1.59 billion vulnerable workers worldwide. These developments will increase global health inequalities, and inequalities between social classes within countries, because unemployment and underemployment cluster among lower income countries and workers. In this article we explore the relation between unemployment, poor working conditions, and health, and argue that governments and public health agencies should recognise that fair employment conditions should be regarded as a human right.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".