Setting research priorities to achieve long-term health targets in Iran
Bibliographic record
Abstract
BACKGROUND: In 2015, it was estimated that the burden of disease in Iran comprised of 19 million disability-adjusted life years (DALYs), 74% of which were due to non-communicable diseases (NCDs). The observed leading causes of death were cardiovascular diseases (41.9%), neoplasms (14.9%), and road traffic injuries (7.4%). Even so, the health research investment in Iran continues to remain limited. This study aims to identify national health research priorities in Iran for the next five years to assist the efficient use of resources towards achieving the long-term health targets. METHODS: Adapting the Child Health and Nutrition Research Initiative (CHNRI) method, this study engaged 48 prominent Iranian academic leaders in the areas related to Iran's long-term health targets, a group of research funders and policy makers, and 68 stakeholders from the wider society. 128 proposed research questions were scored independently using a set of five criteria: feasibility, impact on health, impact on economy, capacity building, and equity. FINDINGS: The top-10 priorities were focused on the research questions relating to: health insurance system reforms to improve equity; integration of NCDs prevention strategy into primary health care; cost-effective population-level interventions for NCDs and road traffic injury prevention; tailoring medical qualifications; epidemiological assessment of NCDs by geographic areas; equality in the distribution of health resources and services; current and future common health problems in Iran's elderly and strategies to reduce their economic burden; the status of antibiotic resistance in Iran and strategies to promote rational use of antibiotics; the health impacts of water crisis; and research to replace the physician-centered health system with a team-based one. CONCLUSIONS: These findings highlight consensus amongst various prominent Iranian researchers and stakeholders over the research priorities that require investment to generate information and knowledge relevant to the national health targets and policies. The exercise should assist in addressing the knowledge gaps to support both the National General Health Policies by 2025 and the health targets of the United Nations' Sustainable Development Goals by 2030.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.070 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".