A model of financial support for the poor to access health services in Iran: Delphi technique
Bibliographic record
Abstract
Purpose Reducing inequity in health between the poor and the rich is one of the challenges of the Iranian health sector. Access to health services in Iran is lower in the lowest-income quarter, and the rich use health services more. The purpose of this study is to provide a comprehensive framework for enabling financial access by the poor to health services in Iran. Design/methodology/approach Policy options were validated and approved by experts and specialists in two stages using the Delphi technique. The sample was consisted of 22 well-known experts on the subject who were selected based on purposive sampling. To evaluate the reliability of the questionnaire, a pilot study was conducted with five participants. Dimensional validity of the policy model, which was agreed upon by more than 75% of the participants was acceptable. Findings The main aspects of the model were divided into five categories: identifying the poor, policymaking to prevent the aggravation of health poverty, providing targeted funding, highlighting the importance of coherent regulation and ensuring financial accessibility to health services for the poor. This model could align the activities of all stakeholders in the form of a network and considers its prerequisites. Originality/value Prevention of dire financial consequences in the case of referral to follow up the treatment alongside exemption and financial protection policies through the networking activities of organizations involved in this field is a crucial step in securing financial support for the poor. Although the researchers included a wide range of policymakers in the Delphi study to gather all perspectives about options for financially support the poor, there may be some potential neglected policy advices.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".