Socioeconomic Inequalities in Different Types of Disabilities in Iran
Bibliographic record
Abstract
Background: This study measured socioeconomic inequalities in different types of disabilities in Iran. We also examined the prevalence of disabilities across different socio-demographic groups in Iran in 201 Methods: This was cross-sectional study using secondary data analysis on all Iranian. Data related to disability prevalence and socioeconomic status (SES) of each province was extracted from the 2011 National Census of Population and Housing (NCPH) and the 2011 Households Income and Expenditure Survey (HIES), conducted by Statistical Center of Iran (SCI). The concentration index and concentration curve were used to measure and illustrate socioeconomic inequalities in different types of disabilities. Chi-squared test was also used to examine the relationship between the socio-demographic variables (age-groups, sex, education level, employment status) and disability. Results: The results suggested the existence of socioeconomic inequalities in blindness, deafness, vocal disorders and hand disorders in Iran. The concentration index for these four disabilities were -0.0527 (95% confidence interval [CI]: -0.0881, -0.0173), -0.0451 (CI: -0.0747, -0.0156), -0.0663 (CI: -0.1043, -0.0282) and -0.0545 (CI: -0.0940, -0.0151), respectively. There were also significant associations between the demographic variables such as age-groups, sex, education level, employment status and disability (P<0.05). Conclusion: There were significant socioeconomic inequalities in different types of disabilities in Iran with poorer provinces having higher prevalence of disabilities in blindness, deafness, vocal disorders and hand disorders. Strategies to address the higher prevalence of different types of disabilities among poorer provinces should be considered a priority in Iran.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".