Socioeconomic Inequalities in Poor Health-Related Quality of Life in Kermanshah, Western Iran: A Decomposition Analysis.
Bibliographic record
Abstract
BACKGROUND: Socioeconomic status (SES) is an important determinant of health-related quality of life (HRQoL). We aimed to quantify socioeconomic-related inequality in poor-HRQoL among adults in Kermanshah, western Iran. STUDY DESIGN: A cross-sectional study. METHODS: Overall, 1730 adults (18-65 yr) were selected using convenience sampling from Kermanshah, Iran. A self-administrated questionnaire was used to collect data on socio-demographic characteristics, SES, lifestyle factors and HRQoL of participants over the period between May and Aug 2017. The concentration curve and concentration index (C) were used to illustrate and measure wealth-related inequality in poor-HRQoL. Additionally, we decomposed the C index to identify factors explaining wealth-related inequality in poor-HRQoL. RESULTS: The overall prevalence of poor-HRQoL was 35.3% (95% confidence interval[CI]: 33.1%, 37.6%). The poor-HRQoL was mainly concentrated among the poor adults (C=-0.256, 95% CI: -0.325, -0.187). Poor-HRQoL was concentrated among men (C=-0.256, 95% CI: -0.345, -0.177) and women (C=-0.261, 95% CI: -0.310, -0.204). Wealth, physical inactivity, the presence of chronic health condition(s), lack of health insurance coverage were the main factors contributing to the concentration of poor-HRQoL among socioeconomically disadvantaged adults. CONCLUSIONS: Socioeconomic-related inequalities in poor-HRQoL among adult should warrant more attention. Policies should be designed to not only improve HRQoL among adults but also reduce the pro-rich distribution of HRQoL among adults in Kermanshah.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".