Unemployment and Health: Contextual Level Influences on the Production of Health in Populations
Bibliographic record
Abstract
While there is a large and growing literature investigating the relationship between an individual's employment status and health, considerably less is known about the effect on this relationship of the context in which unemployment occurs. The aim of this paper is test for the presence and nature of contextual effects in the ways unemployment and health are related, based on a simple underlying model of stress, social support and health using a large population health survey. An individual's health can be influenced directly by own exposure to unemployment and by exposure to unemployment in the individual's context, and indirectly by the effects these exposures have on the relationship between other health determinants and health. Based on this conceptualization an empirical model, using multi-level analysis, is formulated that identifies a five-stage process for exploring these complex pathways through which unemployment affects health. Results showed that the association of individual unemployment with perceived health is statistically significant. Nevertheless, this study did not provide evidence to support the hypothesis that the association of unemployment with health status depends upon whether the experience of unemployment is shared with people living in the same environment. Above all, this study demonstrates both the subtlety and complexity of individual- and contextual-level influences on the health of individuals. Our results caution against simplistic interpretations of the unemployment-health relationship and reinforce the importance of using multi-level statistical methods for investigation of it.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".