Non-communicable diseases challenges and opportunities in Iran: a qualitative study
Bibliographic record
Abstract
Policymakers should focus on reducing risk factors and ensuring equitable access to preventive and therapeutic care for NCDs. This study aimed to identify health promotion challenges and opportunities for NCDs in Iran. This qualitative study involved semistructured interviews with 14 participants, including policymakers and experts in obesity, diabetes, hypertension, and cardiovascular disease management in Iran. Interviews were conducted via Skype, recorded, and transcribed. A deductive approach was applied to extract codes using MAXQDA 10 through open coding. Qualitative analysis identified five main categories and 14 subcategories of challenges and opportunities for addressing NCDs in Iran, aligned with the Ottawa Charter for Health Promotion; policy levers for NCD prevention, including legislation and economic growth creating health-promoting environments, focusing on strengthening physical and social infrastructure community and Individual empowerment for health, focusing on social capital development, public participation, improving the quality of education, and promoting health literacy and transforming Healthcare for better health, organizing health systems and eliminating conflicting interests. The critical important of policies, environmental determinants, community involvement, and healthcare frameworks was highlighted. A holistic approach is essential for the effective prevention and management of NCDs.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".