What the policy and stewardship landscape of a national health research system looks like in a developing country like Iran: a qualitative study
Bibliographic record
Abstract
Abstract Background The health research system (HRS) is an important national priority that requires a systematic and functional approach. Evaluating the HRS of Iran as a developing country and identifying its challenges reveals the stewardship-related role in how the whole system is operating well. This study aims to assess the HRS in terms of stewardship functions and highlight the enhancement points. Methods This study was carried out between March 2020 and April 2021 using a systematic review and meta-synthesis of evidence to examine the Iranian HRS stewardship challenges and interview 32 stakeholders, using a critical case sampling and snowballing approach which included both semi-structured and in-depth interviews. The interviewees were selected based on criteria covering policy-makers, managers, research bodies and nongovernmental organizations (NGOs) in health research-related fields like higher education, research, technology, innovation and science. All data were analysed using content analysis to determine eight main groups of findings under three levels: macro, meso, and micro. Results Analysis of the findings identified eight main themes. The most critical challenges were the lack of an integrated leadership model and a shared vision among different HRS stakeholders. Their scope and activities were often contradictory, and their role was not clarified in a predetermined big picture. The other challenges were legislation, priority-setting, monitoring and evaluation, networking, and using evidence as a decision support base. Conclusions Stewardship functions are not appropriately performed and are considered the root causes of many other HRS challenges in Iran. Formulating a clear shared vision and a work scope for HRS actors is critical, along with integrating all efforts towards a unified strategy that assists in addressing many challenges of HRS, including developing strategic plans and future-oriented and systematic research, and evaluating performance. Policy-makers and senior managers need to embrace and use evidence, and effective networking and communication mechanisms among stakeholders need to be enhanced. An effective HRS can be achieved by redesigning the processes, regulations and rules to promote transparency and accountability within a well-organized and systematic framework.
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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 | 0.030 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".