Arbeitslosigkeit und Gesundheit: ein Überblick über empirische Befunde und die Arbeitslosen- und Krankenkassenstatistik (Unemployment and health: an overview of empirical findings and the unemployment and health insurance statistics)
Bibliographic record
Abstract
"Compared with the employed, unemployed people show a clearly poorer health condition. This is substantiated by representative surveys and by the statistics of the health insurance companies in Germany. International meta-analyses show that long-lasting unemployment does not only correlate with mental illnesses but can also cause or aggravate them. In the official unemployment statistics about a quarter of the unemployed have health-related problems that affect placement. In the routine report procedures of the medical service of the Federal Employment Service (Bundesanstalt für Arbeit) in 2001 'mental illnesses and behavioural disorders' were diagnosed the most frequently after 'illnesses of the muscular-skeletal system'. However, the proportion of unemployed people with health problems is systematically underestimated in the unemployment statistics. As a result of the outflows of unemployed people who are unfit for work and other special groups from the stock, selection effects and artefacts occur in the statistics. In 2001 approximately 76,000 recipients of unemployment benefit or unemployment assistance who were unable to work for health reasons were not counted in the annual average. As a result of the remarks made by the placement officers in the applicant offers, social-medical reports and certificates of incapacity for work, health-related data are available for a large number of unemployed people. The information remains fragmentary, however, and the sources of data are not linked. At the same time unemployed people are taken insufficiently into account in the health reports of the individual health insurance companies and in the national statistics covering all types of health insurance. In addition to this, unemployed people are neglected as a target group for prevention and health promotion. A vicious circle is beginning to emerge for unemployed individuals with health problems, as they have poorer chances of being reintegrated into the labour market and are in greater danger of becoming long-term unemployed. It is therefore essential to develop approaches to health promotion that take the labour market into account. By intensifying the prevention idea in the catalogue of tasks of the health insurance companies and by introducing 'profiling' and 'case management' in the Job-AQTIV law, new opportunities could arise for unemployed people with health problems." (Author's abstract, IAB-Doku) ((en))
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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 | 0.044 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".