Liberal/Individualized Versus Materialist/Structuralist Approaches to Addressing Social and Health Inequalities: Education and Income as Social Determinants of Health
Bibliographic record
Abstract
Background: While consensus exists that the sources of health inequalities are social inequalities brought on by the experience of qualitatively different living and working conditions, means of addressing these conditions continue to be the subject of dispute. Whether to emphasis education or income as a social determinant of health is one such example of differing views on the sources of these inequalities and the means of addressing them. These different emphases are often justified through the narrow examination of the magnitude of statistical relationships between educational attainment and income with health outcomes. Purpose: We offer a broader view, seeing these differing emphases as indicative of contrasting views of the nature of society and means of responding to these inequalities with emphasis on education representing a liberal reformist view of the issue while an emphasis on income representing a materialist structuralist view. Research design and study sample: We examine, the validity of this hypothesis through an analysis of content of five representative publications that consider educational attainment as a social determinant of health and five that do so for income. Analysis and results: We find that the emphasis on education as a social determinant of health focuses on the attributes of the individual and is generally accepting of the structures and processes of the existing economic and political order. In contrast, an emphasis on income – when placed within a materialist analysis – views existing systems as inequitably distributing income and other resources thereby requiring their reform or transformation. Conclusion: Considering evidence of deteriorating living and working conditions for many in Canada and elsewhere, we see the latter emphasis as more useful for understanding and addressing these disturbing developments.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.047 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".