Policies and Programs for Prevention and Control of Diabetes in Iran: A Document Analysis
Bibliographic record
Abstract
Trend analysis in 2005 to 2011 showed high growth in diabetes prevalence in Iran. Considering the high prevalence of diabetes in the country and likely to increase its prevalence in the future, the analysis of diabetes-related policies and programs is very important and effective in the prevention and control of diabetes. Therefore, the aim of the study was an analysis of policies and programs related to prevention and control of diabetes in Iran in 2014. This study was a policy analysis using deductive thematic content analysis of key documents. The health policy triangle framework was used in the data analysis. PubMed and ScienceDirect databases were searched to find relevant studies and documents. Also, hand searching was conducted among references of the identified studies. MAXQDA 10 software was used to organize and analyze data. The main reasons to take into consideration diabetes in Iran can be World Health Organization (WHO) report in 1989, and high prevalence of diabetes in the country. The major challenges in implementing the diabetes program include difficulty in referral levels of the program, lack of coordination between the private sector and the public sector and the limitations of reporting system in the specialized levels of the program. Besides strengthening referral system, the government should allocate more funds to the program and more importance to the educational programs for the public. Also, Non-Governmental Organizations (NGOs) and the private sector should involve in the formulation and implementation of the prevention and control programs of diabetes in the future.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".