Social gradients in child health and development in relation to income inequality. Who benefits from greater income equality
Bibliographic record
Abstract
There is considerable evidence that health and development are better, on average, in countries with greater income equality. However, much of the research has focussed on average health and wellbeing; it is less clear how this benefit is distributed across society – do people from advantaged and disadvantaged socioeconomic backgrounds benefit equally? Further, there has been little research on the relationship between income inequality and child health. \nThis thesis aimed to explore how the social gradient in child health and development varies in relation to income inequality in high income countries. \n \nI used two approaches to answer the question: Does everyone do better in more equal countries? I conducted a critical review of previous literature comparing social gradients in health and wellbeing. I also conducted original analysis using a comparative cohort study. I compared social gradients in health and development among children aged 4-6, using 7 cohort studies from 6 countries (US, UK, Australia, Canada, Netherlands, Sweden). I reviewed approaches to comparing data between studies and across countries, and harmonised the samples and variables to facilitate comparisons. \n \nThe studies in the critical review varied considerably, but there was substantial evidence that health and wellbeing are better for everyone in more equal countries (with the most disadvantaged benefitting the most). In the comparative cohort analysis, there was some evidence that social gradients are steeper in more equal countries (inequalities are greater), and some evidence that everyone does better. However, there were many inconsistencies and comparisons were challenging due to measurement differences between the cohorts. \n \nThe observation that social gradients are shallower in some countries than others shows that such inequalities can be prevented. There is growing evidence that people from all social backgrounds would benefit if countries had greater income equality. \n
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".