User fee removal for the poor: a qualitative study to explore policies for social health assistance in Iran
Bibliographic record
Abstract
INTRODUCTION: Removal of user fee for vulnerable people reduces the financial barriers associated with healthcare payments, which, in turn, improves health outcomes and promotes health equity. This study sought to provide policy strategies to reduce user fee at the point of service delivery for the poor in Iran. METHODS: This is a qualitative study carried out in 2018. The purposive sampling method was applied, and 33 experts with relevant and valuable experiences and maximum variation to obtain representativeness and rich data were interviewed. Trustworthiness criteria were used to assure the quality of the results. The data were analyzed based on thematic analysis using the MAXQDA10 software. RESULTS: The most important issue regarding financial protection against user fee for the poor in Iran is policy integration and cohesion. Differences in access to financial support for user fee coverage among different groups of the poor have led to inequalities in access and financial protection among the poor. The suggested protection policies against the user fee at the point of service delivery in Iran can be categorized into three main categories: 1) basic health social insurance instruments, 2) free health services to the poor outside of the health insurance system, and 3) complementary insurance mechanisms. CONCLUSION: Implementing a cohesive social assistance policy for all disadvantaged groups is needed to address inequalities in financial protection against user fee payment among the poor in Iran. Reducing user fee through mechanisms such as deductible cap, stop-loss, variable user fee and sliding fee scale can improve financial protection and enhance healthcare utilization among the poor. A user fee exemption is not enough to remove barriers to access to service for the poor, as other costs such as transportation expenditures and informal payments also put financial pressure on them. Therefore, financial support for the poor should be designed in a comprehensive protection package to reduce out-of-pocket payments for healthcare services, and indirect costs associated with healthcare utilization.
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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 | 0.024 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".