Designing a referral system management model for direct treatment in social security organization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Aims: The Social Security Organization is the largest provider of health services throughout the country after the Ministry of Health. Lack of the classification and referral system will lead to treatment withdrawal, patients' confusion, immethodical visits and waste of resources. This study was carried out with the aim of proposing a proper model for the management of patients' referral system in the direct care unit of the Social Security Organization. Methods: This comparative study was carried out in 2009. Two separate meetings of the Expert Panel in developing the proposed model were utilized for codification of the model. Then, the thematic and pivotal codes were extracted through framework analysis and the model was proposed according to the research objectives and research findings. This model was approved by experts through three rounds of Delphi method. Results: The classification of services and patient referral system is more comprehensive in US, Canada, Britain, South Korea and Chile due to the presence of national medical system. The referral system is not followed seriously in France. In Austria, there is a universal social insurance structure, but the social insurance system has been partially applied in Turkey. The patient referral system hasn't been performed in the direct care unit of the Iranian Social Security Organization and only the Patient Guidance Staff is responsible for referring the patients to specialized services. Conclusion: The proposed model of managing the referral system in this study is based on a semi-open referral system and the constant presence of family physician with voluntary membership. Financial leverage is used in this model for optimal administration of the referral system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it