Determinants of Socioeconomic Inequalities in Well-being in Canada: Evidence for Nova Scotia Quality of Life Survey
Bibliographic record
Abstract
There are relatively few studies that have measured and explained socioeconomic inequalities in the well-being of populations. Using unique information available in the 2019 Nova Scotia Quality of Life Survey (NSQLS, n=9388), this study provides analysis of the determinants of socioeconomic inequalities in well-being of adults aged 18 and above in Nova Scotia, Canada. The population's well-being was measured using the Canadian Index of Wellbeing (CIW), which encompasses quality of life across eight domains. The Concentration index (C) approach was utilized to quantify and identify factors explaining socioeconomic inequality in well-being. A positive value of the C (0.0294; 95% confidence interval: 0.0267 to 0.0321) indicated pro-rich inequality in well-being among Nova Scotian residents. Results of the decomposition analysis indicated that the concentration of favorable mental health, education levels, and income among high socioeconomic status (SES) groups accounted for over 86% of the observed socioeconomic inequality in the population's well-being. Our findings demonstrated that inequalities in mental health, education, and income are significant obstacles to reducing inequality in well-being in Nova Scotia, Canada. Thus, policies aimed at alleviating inequalities in these factors may help to reduce socioeconomic inequality in well-being in Nova Scotia, 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".