Epidemiological landscape in Iran: A qualitative analysis of strengths, weaknesses, and growth potential
Bibliographic record
Abstract
OBJECTIVES: Epidemiology deals with all aspects of wellness and diseases in communities. Due to the crucial contribution of epidemiology in basic and applied research, operationalization of social and health constellations, and advancing multidisciplinary science, this study aims to Epidemiological Landscape in Iran. STUDY DESIGN: Qualitative study. METHODS: This qualitative study employed semi-structured interviews with a purposive sampling of graduate students, faculty members, and professors with at least five years of experience in epidemiology from various medical universities. The research team, comprising three epidemiologists and one graduate student, ensured the validity of the interview guide. Data saturation was achieved after 17 interviews, focusing on challenges and opportunities in epidemiology, influenced by factors like health ministry policies and societal attitudes. Participants were asked about the ideal state of epidemiology, existing gaps, and their main concerns. The principal investigator confirmed understanding of the concepts presented, and data credibility was assessed through various criteria. The analysis involved coding participants' responses and categorizing them to streamline the findings into main and subcategories. All the qualitative analyses were conducted using MAXQDA (version 2020). RESULTS: The finding highlight epidemiology's critical role in addressing health problems, assessing population health, and diagnosing main issues. The COVID-19 pandemic provided unique opportunities to utilize epidemiological knowledge, which the IEA should amplify by employing epidemiologists in decision-making. However, challenges include limited job/training opportunities. Key limitations are restricting epidemiology to research, isolating it from health decision-making, lack of funding and resources at some universities, and ineffective public communication during pandemic. Strengths are experienced epidemiologists, the IEA, enthusiastic students, and active participation in managing the pandemic. Opportunities include a growing need for epidemiology among policymakers, positive feedback on its role in infectious disease control, and research opportunities for students. CONCLUSIONS: The study identified significant challenges but also strengths and opportunities for epidemiology in Iran. Key recommendations include strengthening collaboration with policymakers, improving education, supporting young epidemiologists, and reinforcing the IEA.
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 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 | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".