Income inequality and deaths of despair risk in Canada, identifying possible mechanisms
Bibliographic record
Abstract
BACKGROUND: Declines in life expectancy in developed countries have been attributed to increases in drug-related overdose, suicide, and liver cirrhosis, collectively referred to as deaths of despair. Income inequality is proposed to be partly responsible for increases in deaths of despair rates. This study investigated the associations between income inequality, deaths of despair risk in Canada, and potential mechanisms (stress, social cohesion, and access to health services). METHODS: We obtained data from the Canadian Community Health Survey and the Canadian Vital Statistics Database from 2007 to 2017. A total of 504,825 Canadians were included in the analyses. We used multilevel survival analyses, as measured by the Gini coefficient, to examine the relationships between income inequality and mortality attributed to drug overdose, suicide, death of despair, and all-cause. We then used multilevel path analyses to investigate whether each mediator (stress, social cohesion, and access to mental health professionals), which were investigated using separate mediation models, influenced the relationship between income inequality and drug overdose, suicide, deaths of despair, and all-cause death. RESULTS: = 1.04; 95 CI = 1.02, 1.07). Adjusted path analyses indicated that stress, social cohesion, and access to mental health professionals significantly mediated the association between income inequality and mortality outcomes. CONCLUSION: Income inequality is associated with deaths of despair and this relationship is mediated by stress, social cohesion, and access to mental health professionals. Findings should be applied to develop programs to address income inequality in Canada.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".