Effect of Income Inequality on Health in Quebec: New Insights from Panel Data
Bibliographic record
Abstract
We investigated the relationship between income inequality and all-cause mortality in 87 regional county municipalities (RCMs) of Quebec (Canada) while accounting for time lags and effects of other socioeconomic variables. We presumed to be true that income inequality entails stress and depression. Thus, these phenomena were tested as mediating factors. The data used consist of eight (8) area-based chronological variables: mortality rate, Gini index, disposable income, criminality rate, number of physicians, density of population, and the proportion of people reporting feeling stressed or depressed. The association between income inequality and mortality was analyzed using the generalized method of moments (GMM) approach with local fixed effects to control unobservable characteristics. Our results show that higher income inequality led to a significant increase of mortality rate with a time lag of 5 years when socioeconomic characteristics were held constant. As expected, households’ disposable income and mortality rate were negatively associated. Moreover, mortality rate was positively associated with population density and negatively associated with the number of physicians. Finally, only depression showed the potential to act as a mediating factor. Based on our findings, we suggest that, over time, income inequality, by amplifying depression phenomena, increases the mortality rate in Quebec’s RCMs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".