Explaining subjective social status and health: Beyond education, occupation and income
Bibliographic record
Abstract
Subjective measures of social status often explain variations in health better than the typical objective measures of education, occupation, and income. This raises the question: if status affects health, then what affects status? To answer this, we ran a survey using representative samples of adult populations in the UK, US and Canada (n = 3,431) to gather data on respondents' subjective social status (SSS) and health-related quality of life (HRQoL), alongside an extensive, rarely gathered set of socioeconomic variables: education, occupation, income, comparative income, wealth, childhood circumstances, parents' education, partner's education, and social and cultural capital. We conduct Shapley-Owen decompositions to identify the relative contributions of these variables in explaining variation in SSS and HRQoL and use RIF (recentered influence function) -regressions to go beyond the mean and identify how these contributions change across the quantiles of SSS and HRQoL. Results show that education, occupation, and income explain relatively little of the explained variation in SSS (26%), while comparative income, wealth and childhood circumstances together explain more than 60%. We find that at higher quantiles of SSS and HRQoL the more subjective and relativistic measures of socioeconomic status contribute more to the explained variation, whilst at lower quantiles, variation is better explained by the more objective socioeconomic variables (i.e. education, occupation, income and wealth). These findings shed light on how policy makers could consider intervening to reduce health inequalities. • Cross-country survey data on health and an extensive set of socioeconomic variables. • Identify contributions of these variables in explaining health and social status. • Education, occupation and income explain relatively little variation. • Comparative income and wealth explain relatively more variation. • Objective socioeconomic variables have higher contributions at lower quantiles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".