Sustainable Development Goals Realisation: A National Indicator Framework for Iranian Health Monitoring
Bibliographic record
Abstract
Sustainable Development Goals (SDGs) provide a global inclusive indicator framework for improving the population’s health, adapted to each country’s socio-political context. This study aimed to propose a national indicator framework for Iran as a reference list toward SDGs realisation in health and health-related. SDGs and three additional complementary frameworks (WHO Core Health Indicators, Action on Social Determinants of Health Core Indicators and Iranian National Health Equity Indicators) were selected to provide the theoretical base for the National Indicator Framework and to identify, compare, and select the potential indicators based on the country’s contextual needs and capacities. WHO’s “result chain pattern for heath core indicators classification” was used as a conceptual basis to facilitate identifying indicators and to link those to underlying country data systems and data gathering methods. After identifying the initial list of 181 indicators, senior informants from the Ministry of Health and Medical Education-related departments and other health-related organisations were consulted to reduce and verify the initial list. A National Indicator Framework for health monitoring in Iran has been developed to contain 101 indicators (including 12 input/ process indicators, 13 output indicators, 44 outcome indicators, and 32 impact indicators) organised within four domains of “health status”, “risk factors”, “service coverage” and “the health system”. This framework addresses the health core indicators gap identified in paragraph No. 3 under article NO.7 of the Law on Permanent Provisions of Country Development Programs. It will be used to notify policies and programs to improve the health system and population health status at the national level.
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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 | 0.033 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".