Healthcare utilization among people with disabilities in Iran: what predictors are associated with medical visits?
Bibliographic record
Abstract
BACKGROUND: Ensuring equitable access to healthcare services for individuals with disabilities poses a significant challenge for healthcare systems. This research aimed to explore the factors affecting medical visits among this population. METHOD: This cross-sectional study in Iran involved data from 766 adults with disabilities aged 18 and older. Unadjusted and adjusted logistic regression analyses were used to calculate the odds ratios for medical visits. RESULTS: The majority of participants were male (64.36%) and single (54.02%). In the adjusted model, participants with severe disabilities (OR: 1.901, p = 0.025) were more likely to utilize medical visits compared to those with less severe disabilities. Conversely, individuals in the second (OR: 0.420, p = 0.017), fourth (OR: 0.360, p = 0.004), and fifth (OR: 0.319, p = 0.001) wealth quintiles demonstrated a significantly lower likelihood of accessing medical visits in comparison to the reference group. CONCLUSIONS: This study reveals critical disparities in healthcare access for individuals with disabilities in Iran. While individuals with severe disabilities demonstrate a higher likelihood of utilizing medical services, those in lower wealth quintiles face significant barriers to accessing care. These findings emphasize the urgent need for targeted interventions to enhance healthcare equity, ensuring that financial constraints do not hinder medical visits for this vulnerable population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".