Factors Affecting Health Care Utilization in Tehran
Bibliographic record
Abstract
INTRODUCTION: Successful health system planning and management is dependent on well informed decisions, so having complete knowledge about medical services' utilization is essential for resource allocation and health plans. The main goal of this study is identification of factors effecting inpatient and outpatient services utilization in public and private sectors. METHODS: This study encompasses all regions of Tehran in 2011 and uses Urban HEART questionnaires. This population-based survey included 34700 households with 118000 individuals in Tehran. For determining the most important factors affected on health services consumption, logit model was applied. RESULTS: Regarding to the finding, the most important factors affected on utilization were age, income level and deciles, job status, household dimension and insurance coverage. The main point was the negative relationship between health care utilization and education but it had a positive relationship with private health care utilization. Moreover suffering from chronic disease was the most important variable in health care utilization. CONCLUSIONS: According to the mentioned results and the fact that access has effect on health services utilization, policy makers should try to eliminate financial access barriers of households and individuals. This may be done with identification of households with more than 65 or smaller than 5 years old, people in low income deciles or with chronic illness. According to age effect on health services usage and aging population of Iran, results of this study show more importance of attention to aged population needs in future years.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".