Challenges of the Health Research System in a Medical Research Institute in Iran: A Qualitative Content Analysis
Bibliographic record
Abstract
BACKGROUND & AIM: Medical research institute is the main basis for knowledge production through conducting research, and paying attention to the research is one of the most important things in the scientific communities. At present, there is a large gap between knowledge production in Iran compared to that in other countries. This study aimed to identify the challenge of research system in a research institute of medical sciences in Iran. MATERIALS & METHODS: This was a descriptive and qualitative study conducted in the first 6 months of 2013. A qualitative content analysis was conducted on 16 heads of research centers in a research institute of medical sciences. The required data were gathered using semi-structured interviews. The collected data were analyzed using MAXQDA 10.0 software. RESULTS: Six themes identified as challenges of research system. The themes included barriers related to the design and development, and approval of research projects, the implementation of research projects, the administrative and managerial issues in the field of research, the personal problems, publishing articles, and guidelines and recommendations. CONCLUSION: Based on the results of the present study, the following suggestions can be offered: pushing the research towards solving the problems of society, employing the strong executive and scientific research directors in the field of research, providing training courses for researchers on how to write proposals, implementing administrative reforms in the Deputy of Research and Technology, accelerating the approval of the projects through automating the administrative and peer-reviewing processes.
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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 | 0.027 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| 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".