SRH and HrQOL: does social position impact differently on their link with health status?
Bibliographic record
Abstract
BACKGROUND: Self-rated Health (SRH) and health-related quality of life (HRQoL) are used to evaluate health disparities. Like all subjective measures of health, they are dependent on health expectations that are associated with socioeconomic characteristics. It is thus needed to analyse the influence played by socioeconomic position (SEP) on the relationship between these two indicators and health conditions if we aim to use them to study health disparities. Our objective is to assess the influence of SEP on the relationship between physical health status and subjective health status, measured by SRH and HRQoL using the SF-36 scale. METHODS: We used data from the French National Health Survey. SEP was assessed by years of education and household annual income. Physical health status was measured by functional limitations and chronic low back pain. RESULTS: Regardless of their health status, people with lower SEP were more likely than their more socially advantaged counterparts to report poor SRH and poorer HRQoL, using any of the indicators of SEP. The negative impact of chronic low back pain on SRH was relatively greater in people with a high SEP than in those with a low SEP. In contrast, chronic low back pain and functional limitations had less impact on physical and mental component scores of quality of life for socially advantaged men and women. CONCLUSIONS: Both SRH and HRQoL were lower among those reporting functional limitations or chronic low back pain. However, the change varied according SEP and the measure. In relative term, the negative impact of a given health condition seems to be greater on SRH and lower on HRQoL for people with higher SEP in comparison with people with low SEP. Using SRH could thus decrease socioeconomic differences. In contrast using HRQoL could increase these differences, suggesting being cautious when using these indicators for analyzing health disparities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".