Widening health inequalities between the employed and the unemployed: A decomposition of trends in Canada (2000-2014)
Bibliographic record
Abstract
Recent developments in the social epidemiological literature indicate that health inequalities between the employed and the unemployed are widening in many advanced capitalist countries. At present, we know relatively little about why these inequalities are worsening. Drawing on nationally-representative data from the largest health survey in Canada, we explored this question by analyzing changes in self-rated health inequalities between employed and unemployed Canadians from 2000 to 2014. Using a regression-based method that decomposes a given inequality into its component sources, we investigated the extent to which risk factors that account for unemployment-related health inequalities at a single point in time can also explain the extent and direction of change in these unemployment-related health inequalities over time. Our results indicate that relative and absolute health inequalities between employed and unemployed Canadians widened over the study period. Between 2000 and 2014, the prevalence of poor self-rated health among unemployed Canadians increased from 10.8% to 14.6%, while rates among employed Canadians were stable at around 6%. Our findings suggest that the demographic, socioeconomic, and proximal risk factors that are routinely used to explain unemployment-related health inequalities may not be as powerful for explaining how and why these inequalities change over time. In the case of unemployment-related health inequalities in Canada, these risk factors explain neither the increasing prevalence of poor self-rated health among the unemployed nor the growing gap between the unemployed and their employed counterparts. We provide several possible explanations for these puzzling findings. We conclude by suggesting that widening health inequalities may be driven by macrosocial trends (e.g. widening income inequality and declining social safety nets) which have changed the meaning and context of unemployment, as well as its associated risk factors, in ways that are not easy to capture using routinely available survey data.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".